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STAFF AI ENGINEER

New York, United States (On-site / Hybrid)

The Role

We’re hiring a Staff AI Engineer to build and scale production-grade GenAI systems powering enterprise AI applications.

This is a high-ownership IC role focused on LLM systems, agent frameworks, and production AI infrastructure. You’ll turn foundation models into reliable, scalable systems that power copilots, automation, and internal AI workflows.

You’ll operate across architecture and implementation with fast iteration from idea to production.

What You’ll Build

• Production LLM-powered services and applications
• Enterprise copilots and chat-based systems
• Text-to-code and natural language SQL interfaces
• Agent frameworks and tool-using AI systems
• Workflow automation powered by foundation models
• API layers for LLM integration across systems
• Scalable, secure GenAI deployments
• CI/CD pipelines for AI services

What You Bring

• 8-12+ years software or ML engineering experience
• Strong experience building production LLM or ML systems
• Deep understanding of foundation models and LLM APIs
• Strong backend and distributed systems experience
• Experience with AWS production environments
• Kubernetes / containerized deployment experience
• Strong systems thinking (latency, scaling, reliability trade-offs)

Strong Preference For

• ML engineering background over traditional full-stack
• Experience building GenAI or agentic systems in production
• Strong analytical background (PhD-level NLP/CV a plus)

Why This Role

• Build foundational GenAI systems with real enterprise impact
• High ownership across architecture and execution
• Fast iteration cycles from idea to production
• High visibility and technical influence
• Opportunity to shape core AI infrastructure

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STAFF AI ENGINEER

Palo Alto, California, United States (On-site / Hybrid)

The Role

We’re hiring a Staff AI Engineer to build and scale production-grade GenAI systems powering enterprise AI applications.

This is a high-ownership IC role focused on LLM systems, agent frameworks, and production AI infrastructure. You’ll take foundation models and turn them into scalable, reliable systems used for copilots, automation, and internal workflows.

You’ll work across architecture and implementation in a fast-moving environment with rapid production iteration.

What You’ll Build

• Production LLM-powered services and applications
• Enterprise copilots and conversational AI systems
• Text-to-code and natural language SQL systems
• Agent frameworks and tool-using AI systems
• Workflow automation powered by foundation models
• API layers for LLM integration across systems
• Scalable GenAI infrastructure and deployments
• CI/CD pipelines for AI services

What You Bring

• 8-12+ years software or ML engineering experience
• Strong experience building production LLM or ML systems
• Deep understanding of foundation models and LLM APIs
• Strong backend and distributed systems experience
• Experience with AWS production environments
• Kubernetes / containerized deployment experience
• Strong systems thinking (latency, scaling, reliability trade-offs)

Strong Preference For

• ML engineering background over traditional full-stack
• Experience building GenAI or agentic systems in production
• Strong analytical background (PhD-level NLP/CV a plus)

Why This Role

• Build and scale core GenAI infrastructure with real impact
• High ownership from architecture through production
• Fast iteration cycles and strong technical autonomy
• High visibility with senior technical leadership
• Opportunity to shape foundational AI systems at scale

• Opportunity to shape foundational AI systems at scale

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Lead Data Scientist

$175,000- $200,000

Pittsburgh, PA (Onsite)

THE ORGANISATION

A discreet, high‑growth technology group is expanding its advanced analytics and AI leadership capabilities. The organisation operates in a deeply complex environment where modelling, optimisation, and intelligent decision systems play a central role. They are investing heavily in next‑generation platforms that blend predictive modelling, simulation, and large‑scale automation to influence critical strategic choices.

THE ROLE

This is a senior technical and leadership role responsible for architecting sophisticated simulation, optimisation, and decision‑intelligence systems. You will guide the vision, design, and delivery of large‑scale AI solutions, combining hands‑on expertise with strategic leadership.

Your remit spans the development of system‑level simulations, optimisation engines, and complex modelling frameworks that support high‑impact decision workflows. You will define long‑range technical direction, lead major AI initiatives end‑to‑end, and mentor a highly skilled team of Data Scientists and AI Engineers.

This is a position for someone who thrives in ambiguity, enjoys solving multi‑variable, system‑level challenges, and brings strong conceptual and practical knowledge of advanced modelling.

KEY RESPONSIBILITIES

• Lead the architecture, design, and deployment of advanced simulation and optimisation platforms
• Build multi‑modal modelling systems incorporating predictive analytics, stochastic simulation, and strategic optimisation workflows
• Define the long‑term technical roadmap for modelling and decision‑intelligence initiatives
• Oversee large‑scale AI projects from initial scoping through to production launch
• Act as the senior technical authority on simulation, optimisation, and model strategy
• Develop scalable frameworks that support complex capability modelling and scenario analysis
• Lead the development of platform components that integrate simulation, optimisation, predictive modelling, and decision logic
• Mentor senior technical team members, raising the standard across modelling, experimentation, and delivery
• Establish modelling best practices, evaluation standards, and robust experimentation workflows
• Partner with cross‑functional teams to convert abstract, open‑ended challenges into concrete technical solutions
• Bring emerging research and methodologies (simulation techniques, optimisation methods, advanced model reasoning, etc.) into production systems
• Communicate modelling strategy, risk, and value clearly to both technical and non‑technical stakeholders

WHAT YOU BRING

• 7+ years of applied Data Science or similar experience in complex modelling domains
• Deep experience with simulation methodologies (e.g. discrete‑event, agent‑based, Monte‑Carlo, or similar approaches)
• Strong understanding of stochastic optimisation, Bayesian optimisation, and advanced mathematical modelling techniques
• Ability to design, evaluate, and scale large‑scale modelling systems and optimisation pipelines
• Strong Python engineering foundations and experience delivering production‑ready AI/ML systems
• Experience leading full lifecycle AI/DS projects, from ideation to deployment
• Proven track record of working with large datasets, complex modelling environments, and multi‑component systems
• Ability to mentor senior‑level technical staff and influence high‑level decision‑making
• Comfortable operating in a high‑autonomy, high‑ownership environment

DESIRABLE EXPERIENCE

• Experience with causal modelling, decision science, or structural modelling frameworks
• Experience with complex system evaluation, simulation benchmarking, or modelling validation
• Background in scaling AI/ML or optimisation systems across cloud environments
• Experience converting experimental prototypes into robust, enterprise‑ready solutions
• Strong publication or research contributions in modelling, optimisation, or related fields

THE OFFER

• Competitive Base Salary
• Opportunity to architect foundational modelling and optimisation systems for a major technology programme
• Significant autonomy and technical ownership
• Ability to shape modelling strategy, culture, and long‑term system design
• Work within a deeply technical team solving highly complex, meaningful challenges

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Forward Deployed Senior Data Scientist

Salary: $175,000+ (Depending on Experience)

Locations: Remote (with 25% travel)

THE COMPANY

A high‑growth, mission‑focused technology organisation is expanding its advanced analytics and AI function. Operating in a complex, data‑rich environment, the team builds scalable, high‑impact AI systems that support critical decision‑making and deliver tangible operational value. The environment is fast‑paced, innovative, and ideally suited to someone who thrives in high‑autonomy, high‑impact roles.

THE ROLE

As a Forward Deployed Senior Data Scientist, you will work directly with stakeholders to translate complex problems into deployable AI solutions. You’ll use a combination of proprietary and commercial datasets to uncover insights, build models, and deploy production‑ready systems that support strategic decision‑making.

This role blends hands‑on data science, AI engineering, rapid prototyping, and end‑to‑end delivery ideal for someone who is highly analytical, scrappy, and passionate about solving complex technical challenges with real‑world impact.

KEY RESPONSIBILITIES

• Build advanced AI/ML/DS models and systems tailored to high‑value operational challenges
• Develop new AI‑driven technology stacks designed for specific customer use cases
• Lead end‑to‑end delivery of AI projects, from scoping to production deployment
• Partner with research, engineering, and software teams to deliver robust AI products
• Define architectural standards and project roadmaps for scalable AI systems
• Create evaluation frameworks for AI deployments, including performance, reliability, and observability
• Mentor junior team members and help shape a culture of excellence and innovation
• Stay current with emerging techniques in AI, LLMs, and agentic systems; drive adoption of best practices
• Build agentic AI systems leveraging reasoning frameworks and orchestrators
• Act as a technical leader, translating high‑level objectives into actionable technical plans

EXPERIENCE REQUIRED

• Extensive experience designing, developing, and delivering AI/ML solutions end‑to‑end
• Strong software and data science fundamentals (Python essential)
• Experience building production‑grade AI systems and integrating them into customer environments
• Ability to collaborate across research, engineering, and product teams
• Experience working with large‑scale datasets and cloud‑based ML deployments
• Strong organisational skills and the ability to communicate complex ideas clearly
• Proven ability to operate independently in fast‑moving environments and manage competing priorities
• Passion for rapid prototyping, experimentation, and building high‑impact solutions

DESIRABLE EXPERIENCE

• Publications in top AI/ML/NLP or decision science venues
• Experience developing evaluation systems for LLMs or agentic models
• Experience with fine‑tuning, post‑training, or advanced model adaptation
• Familiarity with MLOps, monitoring, deployment, and large‑scale model management
• Track record of converting research prototypes into production systems

THE OFFER

• Competitive compensation (around $175,000 base depending on experience)
• Opportunity to work on deeply impactful, high‑scale AI challenges
• High degree of autonomy, technical ownership, and end‑to‑end influence
• A fast‑paced, innovation‑driven environment with strong technical peers
• Exposure to cutting‑edge modelling, LLMs, and agentic system development

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Director, Legal Innovation and AI Strategy (& various related roles)

Location: New York, Boston, San Francisco, or other major U.S. offices

About the Opportunity

We are partnering with a globally leading law firm at the forefront of AI, legal engineering, and innovation.

This firm is making a significant, long-term investment in AI-enabled transformation across its practice groups, building a best-in-class capability spanning legal engineering, knowledge systems, workflow automation, and client-facing innovation.

They are widely recognized as a market leader in advising the most sophisticated technology and AI companies in the world, and are now scaling internal capabilities to match that external leadership.

This is a rare opportunity to define how AI reshapes legal practice at an institutional level.

The Role

This Director will operate at the intersection of client strategy, legal practice, and AI-enabled delivery, helping shape and scale the firm’s legal innovation function.

You will lead initiatives that translate complex legal work into structured, AI-enabled systems, while also partnering directly with senior attorneys and clients to articulate the firm’s AI-driven value proposition.

This role combines strategic leadership with hands-on execution, and will play a central role in building and mentoring a multidisciplinary team of legal engineers, innovation professionals, and domain experts.

Key Responsibilities

• Lead the development and execution of AI-driven legal innovation strategy across multiple practice areas
• Partner with senior attorneys and client teams to define and communicate AI-enabled client solutions and differentiation
• Oversee the design and deployment of AI-powered workflows across litigation, transactional, and knowledge domains
• Build and scale a team of legal engineers, workflow specialists, and innovation professionals
• Translate legal processes into structured systems, prompt frameworks, and scalable AI workflows
• Drive adoption of AI tools through training, change management, and stakeholder engagement
• Evaluate and partner with external vendors to ensure access to best-in-class AI tooling
• Establish frameworks for quality, governance, and consistency of AI outputs
• Operate as a key bridge between legal practitioners, technical teams, and firm leadership

Ideal Candidate Profile

• JD strongly preferred with 10+ years of experience across legal practice, innovation, or legal technology
• Proven experience leading legal innovation, legal engineering, or AI transformation initiatives
• Deep understanding of legal workflows across litigation and/or transactional practices
• Demonstrated ability to translate complex legal reasoning into structured, scalable systems
• Experience working with generative AI, prompt engineering, or workflow automation in a professional setting
• Strong leadership experience managing multidisciplinary teams
• Exceptional communication skills with the ability to engage credibly with senior partners and clients

What Sets This Role Apart

• Opportunity to shape firm-wide AI strategy at a top-tier global law firm
• Direct exposure to high-value client engagements and innovation-driven business development
• Ability to build and scale a next-generation legal engineering function
• Work at the cutting edge of AI, legal practice, and client service transformation
• Significant investment and commitment from firm leadership to this function

Who Thrives Here

• Builders who enjoy translating ambiguity into structured systems
• Individuals who can operate across strategy, execution, and communication layers
• Leaders who are equally comfortable engaging with attorneys, engineers, and clients
• Professionals motivated by redefining how legal services are delivered at scale

This role is designed for individuals operating at the very highest level of the market, those who combine rare technical fluency, legal depth, and strategic leadership into truly differentiated impact.

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Senior Data Scientist
AdTech | Fully Remote (US)
Base salary up to $200K + equity

On behalf of a high-growth AdTech startup, we are partnering with an innovative, venture-backed business that is scaling a data‑driven advertising platform operating at significant volume and complexity. The company is building core technology for real-time decisioning, optimisation, and measurement, and is now seeking a Principal Data Scientist / ML Engineer to play a key role in shaping its machine learning and data science capabilities.

This position represents a true hybrid role, split approximately 50% Machine Learning Engineering and 50% Data Science, with end‑to‑end ownership from research through to production.

The Role

The successful candidate will operate at Principal level, acting as both a hands-on technical leader and a strategic partner to Product and Engineering leadership.

Key responsibilities include:

• Owning and leading high-impact ML and data science initiatives across the business
• Designing and scaling production-grade ML architectures using Databricks and cloud-native tooling
• Building, deploying, and maintaining batch and real-time ML pipelines used in customer-facing systems
• Developing advanced statistical and machine learning models to support optimisation, ranking, forecasting, and attribution use cases
• Establishing best practices for experimentation, feature engineering, model deployment, monitoring, and retraining
• Driving MLOps standards across the organisation, including CI/CD for ML, model versioning, and performance monitoring
• Translating complex business problems into robust, data-driven solutions
• Mentoring senior and mid-level data scientists and ML engineers, while setting a high technical bar
• Contributing to the longer-term ML and data strategy in a fast-scaling startup environment

Required Background

• 6-10+ years of experience across Data Science and/or ML Engineering, with demonstrated impact at Staff or Principal level
• Strong hands-on experience building and operating production ML systems at scale
• Deep expertise with Databricks (Spark, MLflow, Delta Lake, feature pipelines)
• Excellent foundations in statistics, machine learning, and applied mathematics
• Experience working with large, complex, real-world datasets in high-throughput environments
• Proven ability to balance research depth with engineering pragmatism
• Comfortable operating in ambiguous, high-ownership startup settings

Preferred Experience

• Background in AdTech, MarTech, marketplaces, or data-intensive SaaS platforms
• Exposure to real-time or low-latency ML systems
• Experience influencing ML platform or data strategy at company level
• Familiarity with GenAI or LLM use cases within advertising or data products

Why This Opportunity

• Join a rapidly scaling AdTech startup at a pivotal stage of growth
• Significant influence over technical direction, architecture, and modeling approaches
• Fully remote role (US-based)
• Competitive compensation package: up to $200K-$210K base + bonus + equity
• High level of ownership and visibility within the business

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Role Title: Staff Data Scientist, Go-To-Market

Compensation: $180,000 – $210,000 USD Base

Location: United States (Remote)

The Opportunity

We’re partnering exclusively with a fast‑growing B2B SaaS company to hire a Staff Data Scientist who will play a central role in shaping how data drives customer growth, retention, and revenue strategy. The business is at an inflection point, intentionally moving from descriptive analytics toward predictive, model‑driven decision making across its go‑to‑market organization.

This is a rare opportunity to step into a high‑impact, senior individual contributor role where the models you build directly influence how teams prioritize accounts, engage customers, and allocate resources.

About the Role

The team is looking for a Staff Data Scientist who can operate across two complementary problem spaces. The first is predictive customer analytics: building robust models for churn, retention, expansion, and early‑lifecycle lifetime value. The second is stakeholder‑embedded problem solving: translating ambiguous commercial questions into scalable data science solutions that teams actually use.

This role is intentionally hands‑on and model‑heavy. You’ll own work from problem framing and feature discovery through model development, validation, and production handoff. While you won’t manage people, you’ll be expected to lead technically, mentor teammates, and help set the standard for applied data science. This is a US‑remote role with regular collaboration across commercial teams.

What You’ll Be Doing

• Design, build, and iterate on predictive models for customer churn, retention, and expansion
• Develop early‑signal frameworks to estimate customer lifetime value well before traditional cohort analysis
• Build customer segmentation models that inform prioritization and targeting across go‑to‑market teams
• Partner closely with Customer Success, Account Management, and Marketing to translate ambiguous questions into modeling solutions
• Incorporate uplift and attribution concepts to connect customer actions and interactions to expected outcomes
• Validate model performance, monitor outcomes, and continuously improve prediction quality
• Collaborate with data engineering to support batch deployment and operationalization of models
• Mentor analysts and peers on modeling approaches, feature engineering, and best practices

What They’re Looking For

• 6+ years of experience in data science with ownership over impactful machine learning projects
• Strong experience building models for churn, retention, customer lifetime value, or segmentation
• Advanced proficiency in Python and SQL
• Demonstrated experience deploying machine learning models into production or operational workflows
• Solid understanding of B2B SaaS business models and sales cycles
• Ability to communicate complex model outputs clearly to non‑technical stakeholders
• Proven ability to operate independently, owning problems end‑to‑end in ambiguous environments

Nice to Have

• Exposure to analytical engineering or ML production architecture
• Experience working alongside data engineering teams on model deployment
• Familiarity with causal inference or marketing effectiveness modeling
• Comfort using modern developer tooling to accelerate modeling workflows

• Seniority Level

  Mid-Senior level

• Industry

  • Software Development
  • IT Services and IT Consulting

• Employment Type

  Full-time

• Job Functions

  • Engineering
  • Information Technology

• Skills

  • Data Science
  • Statistics
  • Machine Learning
  • Java
  • Software Development
  • Data Analytics
  • Data Analysis
  • Analytics
  • SQL
  • Algorithms
  • Python (Programming La

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Staff Data Scientist

Location: New York City (Hybrid)

Compensation: Up to $250,000 base + bonus + equity

Company Overview

A high-growth consumer fintech and e-commerce platform is building the credit infrastructure powering digital commerce in a large, underserved market. The business has reached profitability, processes hundreds of millions in annual transaction volume, and continues to scale rapidly with strong backing from top-tier investors.

The team is lean, highly technical, and composed of leaders from globally recognized technology and marketplace companies. This is an opportunity to join at a pivotal stage and directly influence core revenue-driving systems.

The Role

As a Staff Data Scientist, you will play a critical role in developing and deploying machine learning models that directly impact the company’s P&L. You’ll work across credit risk, pricing, and marketplace optimization problems, owning the full lifecycle from problem definition through to production.

This is a highly cross-functional role partnering with engineering, product, and leadership to drive data-informed decisions and scalable modeling solutions.

Key Responsibilities

• Build and deploy machine learning models for underwriting, credit risk, and portfolio optimization
• Develop pricing, ranking, and personalization algorithms to improve marketplace performance
• Apply causal inference and experimentation techniques to optimize decision-making
• Own projects end-to-end: from exploratory analysis and modeling through to production deployment
• Translate complex modeling outputs into clear business insights and recommendations
• Collaborate closely with engineering and product teams to operationalize models

Requirements

• 5+ years of experience in data science or machine learning in a production environment
• Strong foundation in statistical modeling and machine learning (e.g., classification, ensemble methods)
• Experience deploying models into production and iterating based on real-world performance
• Proficiency in Python and SQL
• Experience with experimentation, causal inference, or uplift modeling
• Strong problem-solving skills with the ability to operate in ambiguous, fast-paced environments

Preferred Background

• Advanced degree (PhD or Master’s) in a quantitative field such as Statistics, Mathematics, Economics, or Operations Research
• Experience in fintech, lending, or credit risk modeling
• Exposure to marketplace, pricing, or recommendation systems
• Familiarity with optimization techniques and constrained modeling problems

What Makes This Opportunity Unique

• Direct ownership of models that impact revenue and risk
• High visibility role working closely with senior leadership
• Fast-paced, startup environment with significant autonomy
• Opportunity to shape core data science strategy and systems

• If you’re excited by building high-impact machine learning systems in a fast-moving environment and want to see your work directly drive business outcomes, this is a unique opportunity to do so at scale.

To Apply for this Job Click Here

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Senior Data Scientist

Manchester (2 days on site)

A high‑growth SaaS scale‑up is hiring a Senior Data Scientist to build their first production AI capabilities. You will work end to end across ideation, prototyping, and deployment, shaping how customers detect fraud, block invalid traffic, and optimise real‑time behavioural analytics.

The Company

They are a rapidly scaling SaaS business specialising in privacy‑compliant, cookieless analytics.

They process billions of data rows weekly to prevent wasteful and fraudulent traffic.

Their platform uniquely combines real‑time behaviour modelling, fraud detection, and optimisation.

The Role

* Deliver full lifecycle AI and ML projects from prototype to production.

* Build applied AI workflows including LLMs, NLP, and RAG‑based solutions.

* Work with large and messy tabular datasets to create high‑impact models.

* Develop classification and optimisation approaches to enhance customer outcomes.

* Partner with Product and Engineering to integrate models into the platform.

* Lead the transition toward a more AI‑driven product ecosystem.

Your Skills and Experience

* Strong commercial experience deploying AI and ML systems into production.

* Hands‑on experience with LLMs, NLP, or retrieval‑augmented workflows.

* Confident working with large‑scale tabular datasets and complex data environments.

* Cloud experience, ideally AWS, plus familiarity with MLOps practices.

* Background in product‑focused environments or consultancy.

* Ability to evidence measurable business impact from previous technical projects.

What They Offer

* Hybrid working with two office days weekly.

* High levels of ownership across AI strategy and delivery.

* Opportunity to build the company’s first production AI systems.

* Collaborative culture within a scaling SaaS environment.

* Clear progression as the AI function grows.

How to Apply

To register your interest, please apply with your CV.

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Senior Data Scientist

London Hybrid (2 days x week)

This is an opportunity to join a growing Data and AI business building production grade machine learning and LLM driven products for financial services and trading environments. The role offers exposure to both proprietary software and consultancy led problem solving, with real ownership over models that move into live use rather than staying at proof of concept stage.

The Company
They are a well established Data and AI organisation that has been operating successfully for several years, combining proprietary tooling with consultancy expertise. Their products support data driven decision making across financial services, including advanced analytical assistants and conversational AI solutions. The business is technically led, collaborative, and focused on building high quality, scalable machine learning systems.

The Role
You will work as part of a close knit data science team, contributing to the design, development and deployment of machine learning and LLM based solutions.
Responsibilities include:

• Designing and building machine learning models used by trading and financial services clients.
• Developing NLP and LLM powered chat engines that surface insights and factual answers from complex data.
• Fine tuning deep learning and large language models, including extraction and parameter optimisation.
• Working end to end on models that are taken through to production rather than remaining as prototypes.
• Contributing to products such as data assistance tools, website chatbots and analytical AI platforms.

Your Skills and Experience

• Hands on experience building and deploying machine learning models end to end.
• Experience with LLMs, NLP, conversational AI, RAG pipelines and model fine tuning.
• Understanding of production grade ML workflows and CI CD principles.
• Experience building chatbots or NLP driven chat engines used in live environments.
• Comfortable working independently and taking ownership of complex problems.

What They Offer

• Hybrid working with two days per week in a London office.
• Remote flexibility for candidates based in the North, with office visits around twice per month.
• The chance to work on impactful AI products used in real commercial and trading environments.
• A supportive team structure with senior mentorship and clear technical progression.

How to Apply
If you are a Senior Data Scientist looking to work on production level ML and LLM solutions within financial services, apply now to find out more.

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Staff AI Engineer

(Hands‑on, LLM Platforms, Technical Lead)

Location: New York
Working model: On‑site
Package: Competitive base + performance‑linked upside

The Opportunity

An established, technology‑driven organisation is building a central AI engineering capability to support complex, data‑intensive decision‑making across the business.

This is a high‑impact individual contributor role for a Senior AI Engineer who enjoys operating close to the commercial core. You’ll take ownership of critical AI systems from architecture through to deployment, working within a small, trusted technical group with direct access to senior stakeholders.

The environment suits someone who thrives in low‑bureaucracy, high‑autonomy settings, where AI is expected to deliver real, measurable value, not experiments for experimentation’s sake.

The Role

You will act as the technical lead for advanced AI tooling, owning system design and delivery across a range of internal use cases.

Responsibilities include:

• Designing and building LLM‑based applications, including retrieval‑augmented generation and agent‑style workflows
• Owning end‑to‑end system architecture, performance, and scalability
• Developing AI tools to support research, monitoring, knowledge discovery, and decision support
• Establishing evaluation and quality frameworks to ensure AI outputs meet high reliability standards
• Reviewing code, setting engineering standards, and supporting the development of other engineers
• Partnering closely with non‑technical users to translate complex workflows into intuitive AI products
• Assessing third‑party tooling and platforms where appropriate

This is a deeply hands‑on role with real ownership.

What We’re Looking For

Core requirements:

• Strong software engineering background (Python preferred)
• Proven experience building production AI systems, particularly modern LLM applications
• Solid understanding of:
  • Prompt design and iteration
  • Embedding pipelines and vector databases
  • RAG architectures and tool‑use patterns
  • Model evaluation and monitoring
• Experience deploying AI products used by non‑technical stakeholders
• Comfortable setting technical direction while remaining a builder

Nice to have:

• Exposure to complex analytical or commercial domains
• Experience working in small, senior engineering teams
• Background in cloud‑native environments

Why Join?

• Ownership of mission‑critical AI systems
• Direct visibility of impact, your work will be used day‑to‑day
• Modern AI stack with freedom to make technical decisions
• Long‑term scope to shape and grow the AI engineering function

To Apply for this Job Click Here

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Senior AI/ML Engineer – Foundation Models & Agentic AI
Life Sciences AI | Biotech / Therapeutics / Computational Biology

What You’ll Work On

• Develop and train proprietary foundation models on multi-omics data (genomics, transcriptomics, proteomics, metabolomics)
• Design novel algorithms to integrate and differentiate across omics modalities
• Own the full training pipeline: data ingestion, tokenization, pretraining, and evaluation
• Push the state of the art – we are building models that do not exist yet

Agentic AI for Life Sciences

• Build and deploy AI agents that assist researchers across therapeutic development, computational chemistry, and biology
• Design multi-agent architectures using LangChain, LangGraph, and custom orchestration layers
• Integrate agents with internal databases, experimental systems, and third-party scientific tools
• Develop agentic workflows for use cases in cosmetics R&D, drug discovery, and clinical analysis

Production ML Infrastructure

• Own model deployment, scaling, and serving infrastructure
• Optimize inference using NVIDIA TensorRT-LLM, Dynamo, Triton, and related tooling
• Partner with software engineering on integration into our broader platform
• Maintain reliability, performance, and observability across deployed models

What We’re Looking For

Required

• Strong hands-on ML/AI engineering experience – you write real code, not just direct others
• Deep proficiency in Python and PyTorch – this is non-negotiable
• Experience training, fine-tuning, and deploying foundation models from scratch or from pretrained checkpoints
• Familiarity with the Hugging Face ecosystem (Transformers, Datasets, PEFT, Accelerate)
• Experience with LangChain and/or LangGraph for agentic pipeline development
• Working knowledge of the NVIDIA stack: TensorRT-LLM, Triton Inference Server, Dynamo
• Comfort with large-scale distributed training tools: DeepSpeed, xFormers
• Strong understanding of state-of-the-art model architectures (transformers, SSMs, diffusion, etc.)
• Ability to collaborate across technical and non-technical teams – bio, chem, software, clinical
• Excellent communication skills – you can explain a training run to a chemist

Strongly Preferred

• Experience with omics data (any modality: genomic, proteomic, transcriptomic, metabolomic)
• Background in computational biology, computational chemistry, or bioinformatics
• Prior work in biotech, pharma, or life sciences AI (not required but a plus)
• Experience building or contributing to multi-agent systems in a production environment
• Track record of independent research or open-source contributions in ML

Technical Skills Summary

Core ML / Training

• PyTorch – primary framework
• DeepSpeed – distributed training and ZeRO optimization
• xFormers – memory-efficient attention and transformer components
• Hugging Face Transformers, PEFT, Accelerate
• Training and fine-tuning large language and multimodal models

Inference & Deployment

• NVIDIA TensorRT-LLM – high-performance LLM inference
• NVIDIA Dynamo – inference orchestration and scheduling
• Triton Inference Server – model serving and batching
• Model quantization, distillation, and latency optimization

Agentic & Orchestration

• LangChain / LangGraph – agent design and multi-agent orchestration
• Tool-use, RAG pipelines, and memory systems
• API and system integration across scientific data sources

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