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Staff Software Engineer – AI Platform · Full-Time

About the Role

We’re looking for a Staff Software Engineer to join our AI platform team at a fast-growing, healthcare-focused startup. This is a hands-on technical leadership role – you’ll be the person who dreams up what’s possible, architects the solution, writes the code, and then hands off polished specs

You won’t be managing people. You’ll be doing the work: designing agentic AI workflows, building generative AI features, and thinking creatively about what could make our platform genuinely better for the people using it. If you’ve ever been the person in the room who says “what if we just built it this way” and then goes home and actually builds it – this role is for you.

What You’ll Do

• Design, architect, and implement agentic AI and generative AI workflows that power core platform capabilities
• Take ownership from idea to implementation – whiteboard it, validate it, ship it
• Identify creative, non-obvious opportunities to leverage AI across the platform and bring those ideas to life
• Contribute to marketing technology initiatives and help build commercially viable AI models

What We’re Looking For

• Proven experience building agentic AI systems or AI-powered chatbots from scratch – not just integrating APIs, but architecting the underlying system
• Strong Python skills across relevant frameworks – Django, Flask, and/or FastAPI
• Comfort with the full development lifecycle: design, implementation, testing, validation, and iteration
• Experience building and deploying on AWS
• Ability to code without relying on AI-assisted tooling – you understand what you’re writing and why
• A builder mentality – you move fast, think creatively, and take pride in shipping things that work
• Frontend experience with React is a plus, though backend depth is what matters most here

Nice to Have

• Experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar agentic frameworks
• Background in healthcare, health tech, or a similarly regulated industry
• Experience working with distributed or cross-timezone engineering teams

Why This Role

• Greenfield AI work – you’ll have real influence over what gets built and how
• Collaborate with a tight-knit, high-trust international team
• Meaningful domain – the work you do will have a direct impact on how healthcare is delivered
• Salary range up to 350k for the right level

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ML Scientist / Researcher

Oncology AI · Foundation Models · Life Sciences

Remote

About the Role

We are building foundation models trained on human tumor biology – one of the most consequential and technically demanding challenges at the intersection of AI and medicine. As an ML Scientist, you will be a core research contributor designing and training these models across multimodal omics datasets, partnering closely with biologists and fellow research scientists to advance the state of the art in oncology AI.

This is a research-forward role for scientists who want their work to matter. We are looking for people with a track record of research excellence – those who have gone deep on model architecture, training dynamics, and rigorous experimental design. If you have built models from the ground up and published findings, we want to talk.

What You’ll Do

• Design and train large-scale foundation models on multimodal biological datasets, including genomics, transcriptomics, and other omics modalities
• Collaborate deeply with computational biologists, research scientists, and domain experts to translate biological questions into tractable modeling problems
• Drive the full research lifecycle: hypothesis formation, experimental design, model development, and rigorous analysis of results
• Contribute to agentic AI systems that reason over complex biological data
• Communicate findings internally and, where appropriate, through peer-reviewed publication

What We’re Looking For

Must-Haves

• Strong research background, typically evidenced by a PhD in machine learning, computational biology, statistics, physics, or a related quantitative field – or equivalent industry research experience
• Demonstrated ability to build and train models end-to-end, including experimental analysis and iteration
• Research excellence: first-author publications at top ML, AI, or computational biology venues are a strong positive signal
• Deep familiarity with foundation model concepts: pretraining, self-supervised learning, attention mechanisms, and large-scale training
• Comfort working at the intersection of biology and machine learning – even without a formal biology degree

Nice-to-Haves

• Experience with biological or omics data (genomics, proteomics, pathology imaging, etc.)
• Prior work in multimodal learning or multi-omics integration
• Familiarity with agentic AI systems or tool-use frameworks
• Background in oncology or disease biology

What This Role Is Not

This is not a production ML engineering or MLOps role. We are not looking for candidates whose primary experience is model deployment, serving infrastructure, or engineering-heavy systems work. The emphasis here is firmly on research depth and model development.

Compensation & Location

Base Salary: $250,000 – $288,000 (depending on experience) + equity

Location: Remote-friendly; office in South San Francisco, CA

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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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Clinical Data Scientist
San Francisco, California

Remote

$160,000 – $190,000 + Equity

About the Company

This innovative health tech startup is working to improve the oncology drug development process by providing better patient data to drug developers as well as better access to clinical trials for patients. This series B startup is expending to meet demand.

About the Role

As a Clinical Data Scientist, you’ll execute the last leg of the clinical data pipeline by transforming, cleaning, validating, and delivering high‑quality clinical datasets for pharma partners. Heavy collaboration with Clinical Ops, AI Engineering, and Data Delivery.

Role Responsibilities

• Transform raw, abstracted, and AI‑processed data into CDISC SDTM/ADaM datasets
• Program statistical outputs (tables, listings, figures) in SAS/R/Python
• Investigate data anomalies across multiple messy data inputs
• Define data dictionaries and standards before study kickoff
• Handle data in a HIPAA‑aligned manner

Key Requirements

• 2-5 years in clinical data science, stat programming, or clinical data management
• Pharma/Biotech experience
• SAS, R, Python, SQL
• Real‑world clinical data or oncology trials
• CDISC SDTM/ADaM

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AI Engineer
San Francisco, California

Remote

$200,000 – $220,000 + Equity

About the Company

This innovative health tech startup is working to improve the oncology drug development process by providing better patient data to drug developers as well as better access to clinical trials for patients. This series B startup is expending to meet demand.

About the Role

As an AI Engineer, you will design and deliver applied AI systems (LLMs/CV/multimodal) that automate clinical variable abstraction and clinical note generation-with repeatable validation, robust documentation, and HITL feedback loops. Heavy emphasis on data engineering and backend rigor to make models usable and efficient.

Role Responsibilities

• Build models and pipelines across EMR/EHR, imaging, clinical reports
• Translate ambiguous clinical requirements into measurable ML objectives
• Define metrics, design experiments, estimate error; review peer work
• Deliver validated AI components for abstraction/note generation; meaningfully reduce manual QA workload via HITL; standardize documentation/testing; establish performant data manipulation patterns (e.g., PySpark, SQL/Postgres) that speed iteration.

Key Requirements

• Python
• Pytorch or Tensorflow
• Data engineering: PySpark, SQL, Postgres, query tuning, data modeling
• Cloud data platforms (e.g., Databricks, S3/Snowflake/Azure/GCP)
• Nice to have experience in oncology/biotech/health tech

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Senior Computer Vision Engineer (2D or 3D – Clinical AI)

About the Company

The mission is simple but ambitious: democratize access to high?quality cardiac care, especially in regions where cardiologists and advanced imaging equipment are scarce.

About the Role

This is a hands-on applied engineering position focused on building and deploying real clinical AI, not academic experiments. You will own production imaging models end?to?end, ensure reliability across diverse clinical settings, and work closely with clinical, regulatory, and deployment teams.

Profile A –

• Segmentation
• Localization
• Ultrasound/Echo image processing
• Signal and noise?robust imaging pipelines

Profile B – 3D Modeling

• Volumetric reconstruction
• 3D geometry and physics-informed modeling
• Computational representations of cardiac structures

Exceptional candidates may be considered for both tracks.

What You Will Own

Computer Vision Development

• Build production-grade CV models used in live cardiac workflows
• Develop 2D segmentation, 3D/4D reconstruction, and mathematical modeling systems
• Optimize models for low?latency clinical deployment
• Debug imaging pipelines across noisy, highly variable datasets

Production ML & Deployment

• Own pipelines from prototype ? production
• Support deployment engineers with real?world constraints
• Maintain model reliability across multiple international regions
• Monitor drift, performance degradation, and operational issues

What We’re Looking For

Required

• Strong experience in Computer Vision (2D and/or 3D)
• Production ML experience-beyond research or academic prototypes
• Background in biomedical or clinical imaging (ultrasound strongly preferred)
• Ability to debug, iterate, and deliver under pressure
• Experience working with deployment or ops engineering teams
• Comfort writing regulatory?oriented technical documentation
• Strong Python + PyTorch skills
• Startup mindset – bias for action, adaptability, ownership

To Apply for this Job Click Here

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The Role

We are looking for a Technical Architect to take joint ownership of two critical dimensions: shaping the architecture of our platform and providing hands-on technical leadership to our engineering team. You will be a key decision-maker in how we build, scale, and evolve our systems – balancing technical excellence with practical delivery.
You will work closely with engineering, AI/ML, and product teams, and you will have a direct impact on the technology roadmap of a fast-growing startup in a highly regulated and meaningful space.

Responsibilities

Systems Architecture

• Design and evolve the overall platform architecture – backend services, ML inference pipelines, and clinical integrations – ensuring scalability, security, and performance.
• Define and enforce architectural patterns, coding standards, and technology decisions with a long-term perspective.
• Evaluate and select technologies, frameworks, and tools, clearly articulating trade-offs to both technical and non-technical stakeholders.
• Ensure architecture decisions account for the regulatory requirements of the medical device space (MDR, FDA SaMD).
• Lead the technical design of integrations with hospital systems (PACS, HIS) using standards such as DICOM and HL7/FHIR.
• Own non-functional requirements: reliability, latency, data privacy (GDPR, HIPAA-aligned), and disaster recovery.

Technical Leadership

• Be the technical reference for the engineering team: code reviews, mentoring, and establishing engineering best practices.
• Collaborate with the AI/ML team on the productionisation of inference models and the optimization of the end-to-end ML pipeline.
• Contribute to sprint planning and technical estimation, helping the team break down complexity into deliverable increments.
• Proactively identify and mitigate technical risks, communicating them clearly before they become blockers.
• Foster an engineering culture centred on quality, automation, observability, and continuous improvement.

Requirements

Must-haves

• 5-8 years of software engineering experience, with at least 2-3 years in architecture or senior technical leadership roles.
• Proven experience designing and operating cloud-native systems on AWS (compute, storage, networking, managed services, IAM).
• Strong background in Python for production environments – APIs, data processing pipelines, and ML model integration.
• Solid understanding of containerisation (Docker) and orchestration in production environments.
• Deep knowledge of distributed systems design principles: microservices, event-driven architecture, API design, and data consistency patterns.
• Experience building CI/CD pipelines and driving DevOps practices across an engineering team.
• Ability to document and communicate architecture decisions clearly, both in writing and in technical discussions.
• Comfortable operating in a startup environment: autonomous, pragmatic, and able to balance ideal solutions with real constraints.

Nice-to-haves

• Experience deploying and optimising ML models in production – including ONNX Runtime or similar inference frameworks.
• Familiarity with medical imaging standards: DICOM, HL7, FHIR.
• Background in regulated environments (MDR, FDA 21 CFR Part 11, ISO 13485, or similar).
• Experience with MLOps practices: model versioning, drift monitoring, and retraining pipelines.
• Knowledge of PHP/Laravel or experience working with and evolving an existing codebase built on it.
• Prior experience in healthtech, medtech, or other highly regulated industries.

What We Offer

• Competitive salary aligned with your experience and the market.
• Hybrid model with real flexibility – we care about outcomes, not hours.
• Direct, visible impact: your work shapes tools that help clinicians make better diagnoses.
• Small, highly skilled team with strong technical autonomy.
• Annual budget for training, conferences, and certifications.
• Freedom to challenge the status quo and propose improvements to how we build things.
• A collaborative, mission-driven environment where engineering is taken seriously.

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Lead AI Engineer

London (Hybrid, 1 to 2 days per week)

The Company
They are a leading UK consumer-focused financial services business with a broad product portfolio and millions of customers. Operating at scale, they are investing heavily in AI to enhance customer experience, drive efficiency, and strengthen risk and fraud capabilities. A new generative AI team is being built to sit within a broader AI and MLOps function, moving from contractor-led delivery to a permanent, strategically aligned capability. This is a high-visibility area with strong internal demand and clear executive backing.

The Role
You will lead the design and delivery of AI solutions that directly impact core operations and customer journeys.

• Own end-to-end delivery of AI systems, from problem definition through to production
• Design and deploy LLM and RAG-based solutions, including chat assistants for customer support and contact centre optimisation
• Drive automation across complaints handling, triage, fraud detection, and customer data workflows
• Define architecture and make decisions around build versus buy solutions
• Work closely with engineering, product, and risk teams to ensure compliant and scalable solutions
• Implement robust MLOps practices including monitoring, evaluation, and system reliability

Your Skills & Experience

• Strong commercial experience delivering production-grade AI systems end to end
• Deep expertise in generative AI, including LLMs, RAG, and agent-based systems
• Solid software engineering background with experience in scalable system design and APIs
• Hands-on experience with MLOps, including deployment, monitoring, and lifecycle management
• Proven ability to lead multiple AI initiatives and deliver measurable business outcomes
• Experience working in large, complex organisations with high data volumes

What They Offer

• Competitive pension and benefits package
• Hybrid working with flexibility
• Opportunity to shape a new AI capability within a large organisation
• Clear progression as the AI function scales

How to Apply
If you are interested in leading AI innovation in a high-impact, enterprise environment, apply now to find out more.

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Senior Data Scientist

London (Hybrid, 2 days onsite)

The Company
They are a large, established organisation operating at scale, with a strong focus on using data and AI to drive innovation across their products and services. The business is undergoing a significant AI transformation, with over ten active AI initiatives spanning areas such as automation, personalisation, and internal tooling. Their data science function is at the core of this journey, working closely with product and engineering teams to deliver tangible impact.
The environment is fast-paced and collaborative, with a strong emphasis on building production-ready AI solutions and embedding them into core business processes.

The Role
As a Senior Data Scientist, you will focus on delivering AI solutions that drive automation and efficiency across the organisation. You will take ownership of key projects and work across the full model lifecycle in a production environment.

• Lead the development and deployment of AI and machine learning solutions across multiple business areas
• Own and deliver end to end AI initiatives, from problem definition through to production
• Work on a range of use cases including internal chatbots, LLM applications, summarisation tools, underwriting automation, and AI sales assistants
• Collaborate with product managers and engineers to ensure solutions are scalable and aligned to business needs
• Implement and maintain MLOps best practices, including monitoring and model lifecycle management
• Engage with senior stakeholders, translating technical outputs into clear business value
• Contribute to the ongoing development of AI capability across the team

Your Skills and Experience

• Strong commercial experience in data science with a recent focus on AI and machine learning
• Proven experience building and deploying production-level AI solutions
• Experience working with GCP and modern cloud-based data environments
• Hands-on experience with MLOps, including deployment, monitoring, and scaling models
• Exposure to generative AI techniques such as LLMs and Retrieval Augmented Generation
• Strong stakeholder management and communication skills across technical and non-technical teams
• Experience working in larger, complex organisations with multiple concurrent workstreams

What They Offer

• Competitive pension scheme
• Private healthcare
• Hybrid working with two days per week in their London office
• Opportunity to work on high-impact AI initiatives with real business visibility
• Clear progression within a growing and strategically important team

How to Apply
If you are interested in applying your data science and AI expertise in a high-impact environment, please get in touch to learn more.

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Analytics Engineer (12-month FTC)

£70,000 – £90,000

Remote (UK Based – meets in Farringdon, London once every 6 months)

A unique opportunity to step into a high-impact Analytics Engineer role within a purpose-driven organisation using data to improve real-world outcomes. This position offers the chance to take ownership of critical data models and pipelines, working in a modern cloud data stack while collaborating closely with engineering and analytics teams. You will play a key role in ensuring continuity across core data products while contributing to new development.

THE COMPANY

They are an e-Health provider operating within a highly regulated, data-rich environment. Focused on delivering meaningful outcomes at scale, they combine clinical expertise with modern data capabilities to continuously improve services. Their data function is central to decision-making, with a strong emphasis on quality, governance, and impact. The team is collaborative, remote-first, and values thoughtful, well-engineered solutions.

THE ROLE

As an Analytics Engineer you will step into an established Analytics Engineering function, maintaining momentum across critical projects while contributing to the ongoing development of the data platform.

Specifically, you can expect to be involved in the following:

• Take ownership of in-flight data projects, ensuring successful delivery and continuity.
• Design, build, and maintain dbt models including fact and dimension tables and semantic layers.
• Support the development of scalable data models with clear structure and strong data governance.
• Collaborate with data engineers to evolve pipelines across the data lifecycle.
• Review and improve SQL and modelling best practices across the analytics team.
• Contribute to the transition from prototype analysis to production-grade data assets.
• Ensure documentation and handover processes are clear and well-structured.

SKILLS AND EXPERIENCE

The successful Analytics Engineer will have the following skills and experience:

• Strong commercial experience working with dbt, including building models and implementing testing frameworks.
• Advanced SQL skills with experience working in modern cloud data environments.
• Solid understanding of data modelling principles including star schema design and data grain.
• Experience working with Python in a data context.
• Ability to collaborate across teams and influence technical decisions constructively.
• Comfortable working within structured, governed data environments
• Desirable experience includes exposure to Databricks, CI/CD workflows, or regulated industry data, though this is not essential.

BENEFITS

The successful Analytics Engineer will receive the following benefits:

• Salary between £70,000 – £90,000 – depending on experience

HOW TO APPLY

Please register your interest by sending your resume to Majid Latif via the Apply link on this page.

To Apply for this Job Click Here

To Apply for this Job Click Here

Analytics Engineer (12-month FTC)

£70,000 – £90,000

Remote (UK Based – meets in Farringdon, London once every 6 months)

A unique opportunity to step into a high-impact Analytics Engineer role within a purpose-driven organisation using data to improve real-world outcomes. This position offers the chance to take ownership of critical data models and pipelines, working in a modern cloud data stack while collaborating closely with engineering and analytics teams. You will play a key role in ensuring continuity across core data products while contributing to new development.

THE COMPANY

They are an e-Health provider operating within a highly regulated, data-rich environment. Focused on delivering meaningful outcomes at scale, they combine clinical expertise with modern data capabilities to continuously improve services. Their data function is central to decision-making, with a strong emphasis on quality, governance, and impact. The team is collaborative, remote-first, and values thoughtful, well-engineered solutions.

THE ROLE

As an Analytics Engineer you will step into an established Analytics Engineering function, maintaining momentum across critical projects while contributing to the ongoing development of the data platform.

Specifically, you can expect to be involved in the following:

• Take ownership of in-flight data projects, ensuring successful delivery and continuity.
• Design, build, and maintain dbt models including fact and dimension tables and semantic layers.
• Support the development of scalable data models with clear structure and strong data governance.
• Collaborate with data engineers to evolve pipelines across the data lifecycle.
• Review and improve SQL and modelling best practices across the analytics team.
• Contribute to the transition from prototype analysis to production-grade data assets.
• Ensure documentation and handover processes are clear and well-structured.

SKILLS AND EXPERIENCE

The successful Analytics Engineer will have the following skills and experience:

• Strong commercial experience working with dbt, including building models and implementing testing frameworks.
• Advanced SQL skills with experience working in modern cloud data environments.
• Solid understanding of data modelling principles including star schema design and data grain.
• Experience working with Python in a data context.
• Ability to collaborate across teams and influence technical decisions constructively.
• Comfortable working within structured, governed data environments
• Desirable experience includes exposure to Databricks, CI/CD workflows, or regulated industry data, though this is not essential.

BENEFITS

The successful Analytics Engineer will receive the following benefits:

• Salary between £70,000 – £90,000 – depending on experience

HOW TO APPLY

Please register your interest by sending your resume to Majid Latif via the Apply link on this page.

To Apply for this Job Click Here

To Apply for this Job Click Here

Analytics Engineer (12-month FTC)

£70,000 – £90,000

Remote (UK Based – meets in Farringdon, London once every 6 months)

A unique opportunity to step into a high-impact Analytics Engineer role within a purpose-driven organisation using data to improve real-world outcomes. This position offers the chance to take ownership of critical data models and pipelines, working in a modern cloud data stack while collaborating closely with engineering and analytics teams. You will play a key role in ensuring continuity across core data products while contributing to new development.

THE COMPANY

They are an e-Health provider operating within a highly regulated, data-rich environment. Focused on delivering meaningful outcomes at scale, they combine clinical expertise with modern data capabilities to continuously improve services. Their data function is central to decision-making, with a strong emphasis on quality, governance, and impact. The team is collaborative, remote-first, and values thoughtful, well-engineered solutions.

THE ROLE

As an Analytics Engineer you will step into an established Analytics Engineering function, maintaining momentum across critical projects while contributing to the ongoing development of the data platform.

Specifically, you can expect to be involved in the following:

• Take ownership of in-flight data projects, ensuring successful delivery and continuity.
• Design, build, and maintain dbt models including fact and dimension tables and semantic layers.
• Support the development of scalable data models with clear structure and strong data governance.
• Collaborate with data engineers to evolve pipelines across the data lifecycle.
• Review and improve SQL and modelling best practices across the analytics team.
• Contribute to the transition from prototype analysis to production-grade data assets.
• Ensure documentation and handover processes are clear and well-structured.

SKILLS AND EXPERIENCE

The successful Analytics Engineer will have the following skills and experience:

• Strong commercial experience working with dbt, including building models and implementing testing frameworks.
• Advanced SQL skills with experience working in modern cloud data environments.
• Solid understanding of data modelling principles including star schema design and data grain.
• Experience working with Python in a data context.
• Ability to collaborate across teams and influence technical decisions constructively.
• Comfortable working within structured, governed data environments
• Desirable experience includes exposure to Databricks, CI/CD workflows, or regulated industry data, though this is not essential.

BENEFITS

The successful Analytics Engineer will receive the following benefits:

• Salary between £70,000 – £90,000 – depending on experience

HOW TO APPLY

Please register your interest by sending your resume to Majid Latif via the Apply link on this page.

To Apply for this Job Click Here