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Lead AI Engineer
Location: San Francisco Bay Area | Hybrid
Salary: $250-300k + Equity

Join a well-funded, AI-native startup at the forefront of redefining how digital products are designed and shipped.

This team is building a next-generation design platform where every screen is real, production-ready code. Designers work directly in the medium that ships, not static mockups, with AI deeply embedded into the workflow to accelerate iteration while preserving precision, consistency, and control.

Backed by top-tier Bay Area investors and senior product leaders from companies including Shopify, Notion, Dropbox, Stripe, and OpenAI, the company has strong runway, a high bar for talent, and ambitious plans ahead.

They’re now hiring their first dedicated Lead AI Engineer to own the model layer and drive AI performance across the product.

The Opportunity

You’ll work directly with the CTO and founding team to define and execute the AI strategy at the model level.

This is a hands-on, production-focused AI engineering role centered on:

• Fine-tuning LLMs for real-world use cases
• Optimizing inference speed, cost, and reliability
• Designing and deploying custom Small Language Models (SLMs)
• Establishing evaluation, benchmarking, and observability standards

What You’ll Be Responsible For

Fine-Tuning & Model Strategy

• Own fine-tuning workflows (LoRA, adapters, distillation, full fine-tuning)
• Decide when to fine-tune vs optimize at the system or prompt level
• Iterate models based on production feedback
• Balance quality, latency, and cost tradeoffs

Model Optimization & Performance

• Improve inference speed and throughput
• Reduce cost per request
• Enhance reliability and output consistency
• Define model evaluation and benchmarking frameworks

Custom SLM Development

• Design and train custom SLMs for specific design-to-code workflows
• Identify when smaller models outperform larger ones
• Deploy and maintain real-time, streaming AI systems
• Support multi-step and agentic AI capabilities within the product

What They’re Looking For

• 5+ years of software engineering experience
• 2+ years building with LLMs in production environments
• Proven hands-on fine-tuning experience (LoRA, distillation, adapters, etc.)
• Experience deploying custom or specialized models
• Strong intuition around inference tradeoffs: latency, throughput, cost, reliability
• Proficiency in Python and TypeScript / Node.js

Nice to have:

• ONNX runtime or model optimization tooling
• Experience with orchestration frameworks (e.g., LangChain)
• WebSockets, Redis, or real-time streaming systems
• Background in AI-native or code-generation products
• A PhD or advanced degree is welcomed, but demonstrated production impact is the priority.

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Staff Data Scientist (Product)

Location: Remote

Salary: $190-220k

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role embedded in their product organization. This is for someone who combines deep statistical expertise with the ability to influence how an entire company makes decisions – not just someone who runs experiments, but someone who builds the culture and infrastructure around them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focused on high-leverage product questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to measure the real impact of product changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to meaningful business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of Product Data, acting as a strategic thought partner across Product, Engineering, Finance, Design, and Product Marketing. The company is at an inflection point in how it uses data to make product decisions, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still developing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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Staff Data Scientist, Finance – Consumption Forecasting

Location: San Francisco or New York | Hybrid (3 days per week in office)

Salary: $240-300k base + bonus + equity (RSUs)

This is a rare chance to own forecasting infrastructure at the center of a high-growth, consumption-based developer platform, one that powers some of the world’s most dynamic applications and scales with every developer and enterprise building on it.

As a consumption-based business, forecasting usage across compute, bandwidth, edge, and storage isn’t a support function. It’s foundational to how we plan infrastructure, revenue, and long-term strategy. This role exists to lead that work at the highest level.

What you’ll own

This is a senior individual contributor role with organization-wide impact. You’ll define forecasting methodology, build systems that scale with a rapidly growing platform, and sit at the intersection of Finance, Infrastructure, Product, and GTM with direct visibility to executive leadership.

• Own production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, and iteration from first principles to live system
• Build forecasting systems that account for usage-based pricing dynamics, consumption patterns, and customer lifecycle across the platform, built for how this business actually works, not retrofitted SaaS models
• Design hierarchical forecasting models across account, cohort, segment, and global aggregate levels, covering operational, quarterly, and long-range planning cycles
• Establish backtesting, monitoring, and explainability standards that make forecast accuracy transparent and defensible
• Build scenario simulation frameworks to evaluate pricing changes, packaging adjustments, and product launches
• Partner with Finance on board-level reporting, with Infrastructure Engineering on capacity planning, and with Product and GTM on adoption curves and usage drivers
• Set forecasting best practices across the broader Data organization

What we’re looking for

• 7+ years in data science, quantitative analytics, or applied statistics at senior or staff level
• Deep expertise in time-series forecasting and statistical modelling in a usage-based or SaaS environment
• Proven track record building and productionizing ML systems at scale
• Strong Python and SQL, with experience on large-scale usage and billing datasets
• Familiarity with probabilistic modelling, hierarchical forecasting, and causal inference
• Experience partnering with Finance or executive leadership on planning cycles
• Comfortable operating autonomously in fast-moving, ambiguous environments

Nice to have

• Background in cloud infrastructure, developer tools, or consumption-based revenue models
• Familiarity with modern data stacks: Snowflake, Delta Lake, dbt, Airflow
• Prior technical mentorship or informal leadership experience

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Staff GTM Data Scientist

Location: Remote

Salary: $190-220k base

We’re partnering with a fast-growing SaaS company to find a Staff Data Scientist for a senior, high-visibility role sitting at the heart of their product and go-to-market strategy. This isn’t a reporting role or a dashboard-builder position. It’s for someone who can drive a genuine shift toward data-driven decision making across the organization, and who has the technical depth and communication skills to bring leadership along with them.

What You’ll Be Doing

• Owning and advancing the company’s experimentation roadmap, focusing on high-leverage questions around customer workflows, churn risk, and long-term value
• Designing and analyzing complex A/B tests, multivariate experiments, and Bayesian methods to assess the real impact of product and business changes
• Applying causal inference techniques (DiD, synthetic control, propensity score matching, instrumental variables) where traditional RCTs aren’t feasible
• Building and governing a unified KPI framework that connects product health metrics to business outcomes
• Partnering with Data Engineering to build scalable, self-serve experimentation tooling and reusable analytical frameworks
• Translating complex statistical findings into clear, compelling narratives for VP and C-suite audiences
• Mentoring and training junior and mid-level data scientists on experimental design and causal modeling

What We’re Looking For

This is a senior individual contributor role reporting to the Director of GTM Data, acting as a strategic thought partner across Product, Marketing, Finance, and Engineering. The company is at an inflection point in how it uses data, and this person will be central to shaping that.

Essential:

• 6+ years in applied data science, economics, or product analytics
• Proven expertise in causal inference: DiD, PSM, instrumental variables, quasi-experimentation
• Deep experience in A/B testing methodology including sequential testing, CUPED, variance reduction, and network effects
• Advanced SQL and Python or R for statistical modeling
• Experience with Snowflake or similar cloud data warehouses
• Exceptional communication skills, comfortable presenting to and influencing C-suite stakeholders
• Demonstrated ability to drive change in organizations where experimentation culture is still maturing

Nice to have:

• Experience with dbt, Airflow, or Databricks
• Background in SaaS and product data science

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Job Title: Staff Data Scientist

Location: Remote (US Only)

Salary: $200-250k base + equity

About the Company:

Join a mission-focused organization dedicated to revolutionizing global education by providing innovative learning experiences beyond the traditional classroom. The company’s app is widely used across U.S. schools and impacts millions of children worldwide.

Their team is made up of talented and creative professionals with backgrounds in education and top consumer internet companies such as Instagram, Netflix, Dropbox, Stripe, and Uber. They cultivate an environment where top talent can thrive. If you’re eager to work with some of the best minds in the industry, we encourage you to apply!

Position Summary:

As a Staff Data Scientist, you will be a key player in developing the world’s leading consumer education platform. You will be part of a high-achieving, cross-functional team working closely with product, engineering, and design to shape the company’s strategic direction and tackle challenging product and business issues.

Key Responsibilities:

• Utilize data-driven insights to inform decisions and drive our brand toward new milestones
• Work collaboratively with various teams to discover user insights and pinpoint essential product improvements
• Design and analyze AB/multivariate tests to derive actionable conclusions that boost user engagement
• Lead data science projects, influencing strategic choices and addressing complex problems

Your Skills and Experience:

• 8+ years of experience in data science and product analytics
• Experience in the consumer technology sector
• Proficient in writing efficient SQL queries for large datasets
• Skilled in designing and analyzing A/B tests
• Strong understanding of growth strategies for consumer products
• Experience working in fast-paced startup environments
• Excellent verbal and written communication skills
• Strategic thinker with a keen focus on product development
• Innovative approach to using data to drive product strategy

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Senior Machine Learning Engineer (Applied ML)

About the Company

Our client is a high-scale consumer technology company leveraging machine learning, advanced analytics, and large-scale data systems to optimize user experiences and drive meaningful business impact. Their products reach millions of users globally, providing an opportunity to work on ML systems at significant scale.

The Role

They are hiring a Senior Machine Learning Engineer to own and improve production machine learning models that directly impact key business metrics. This is a highly applied ML role focused on model performance, experimentation, feature engineering, and continuous optimization-not ML infrastructure.

You’ll work with large-scale datasets to improve prediction, ranking, recommendation, and optimization systems, partnering closely with Product, Data, and Engineering teams to deliver measurable outcomes.

What They’re Looking For

• 5+ years of experience in Machine Learning Engineering, Applied ML, or Data Science
• Strong hands-on Python and SQL skills
• Experience building, deploying, and improving production ML models
• Experience working with large-scale datasets
• Strong understanding of feature engineering, model optimization, and experimentation
• Experience with recommendation systems, ranking, personalization, Ad Tech, search, marketplace optimization, or similar large-scale ML problems
• Ability to operate independently, take ownership, and drive technical decisions

Why Join?

• Work on high-impact ML systems serving millions of users
• Own and improve models that directly influence product performance and revenue
• Join a mature engineering environment with strong data infrastructure already in place
• Operate as a senior individual contributor with significant ownership and impact

Location

Remote within the following states: California, Washington, Texas, Arizona, Utah, Nevada, New Jersey, New York, and Oklahoma.

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VP AI Engineer
$220K base + bonus + RSU
New York or San Francisco

A global financial technology organisation operating at enterprise scale, combining advanced software engineering, data science, and applied AI to modernise large-scale production systems. The company invests heavily in automation, operational excellence, and AI-driven infrastructure, building mission-critical platforms used across complex, high-reliability environments.

Mission

• Design and deploy agentic AI systems to automate and optimise large-scale production environments
• Build LLM-powered solutions that diagnose issues, reason over complex systems, and take secure, auditable actions
• Develop RAG pipelines and domain knowledge systems with strong data quality, feedback loops, and governance
• Productionise LLMs: evaluation frameworks, prompt orchestration, response validation, self-correction loops
• Integrate AI agents with observability, incident management, deployment, and runtime platforms
• Implement safety, reliability, and compliance guardrails (policy enforcement, rollback strategies, circuit breakers)
• Optimise performance, cost, and latency through prompt engineering, caching, routing, batching, and streaming

Profile

• Bachelor’s degree in a quantitative or computational field (Master’s / PhD preferred)
• 7+ years of experience in applied ML, data science, or software engineering in production environments
• Strong hands-on development in Python (or C++ / Java / Go), building large-scale applications
• Practical expertise with LLMs: prompt engineering, fine-tuning/adaptation, RAG, tool-calling agents, vector search
• Experience building secure, explainable, and reliable AI systems with strong governance and auditability
• Comfortable collaborating across engineering, infrastructure, and operations teams with measurable business outcomes

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Senior Machine Learning Engineer

Location: Fully Remote (Hybrid option in NYC)
Compensation: $190,000-$200,000 + Equity
Type: Full-Time

About the Company
We are partnering with a fast-growing, profitable SaaS company that is redefining programmatic advertising through AI-driven solutions. Their platform empowers brands to deploy custom algorithms across major DSPs (The Trade Desk, DV360, Meta, YouTube), enabling smarter, real-time ad-buying decisions. Backed by leading industry investors, this company is building ML infrastructure and solving problems that don’t have off-the-shelf solutions.

Why Join?

• Work on distributed ML infrastructure using Ray + PyTorch on Databricks.
• Build solutions that create a new software layer in ad-tech.
• Flexible working hours, fully remote, and equity participation.
• Opportunity to grow into Principal ML Engineer later this year.

Role Overview

As a Senior Machine Learning Engineer, you’ll own the ML lifecycle end-to-end, focusing on productionization and robust MLOps practices. You’ll work closely with data science and engineering teams to deploy models at scale and build automation for training, inference, and observability.

Key Responsibilities

• Deploy ML models into production using CI/CD best practices.
• Monitor and manage ML drift; retrain models as needed.
• Build automation for ML lifecycle (training, inference, observability).
• Contribute to internal API development for future projects.
• Collaborate across teams to productionize models for client and internal use cases.

Must-Have Skills

• Strong experience with CI/CD (GitHub Actions, build automation, packaging).
• Expertise in MLOps (MLflow, model versioning, monitoring).
• Hands-on with Databricks (Delta Lake, Unity Catalog, Asset Bundles).
• Proficient in Python and PySpark.
• 3-6 years of relevant experience.

Nice-to-Have Skills

• Kubernetes and containerized environments.
• API development exposure.
• Distributed training (Ray) and observability tools (Prometheus, Grafana).
• Familiarity with embedding models and Databricks Clean Room.

Screening Process

1. Intro (with Director of ML or Senior Engineer) + short live coding exercise.
2. Take-home exercise (CI/CD-focused).
3. Final interview with leadership.
4. CEO check-in.

Interested?
Apply now to join a team building brand-new solutions in ad-tech and work with technologies like PyTorch, Ray, Databricks, and MLflow.

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This is an opportunity to work on real-world AI applications from concept through production, leveraging the latest advancements in large language models, conversational AI, retrieval systems, and intelligent automation. The ideal candidate is a hands-on builder with recent experience deploying production-grade GenAI solutions and thrives in a fast-paced, collaborative environment.

Responsibilities

• Design, prototype, and deploy Generative AI solutions across customer-facing and internal business applications.
• Develop and optimize applications powered by large language models (LLMs), vector databases, prompt engineering techniques, and Retrieval-Augmented Generation (RAG) architectures.
• Build, deploy, and maintain AI-powered chatbots and virtual assistants, ensuring scalability, reliability, security, and compliance.
• Lead the development of AI agents for both digital and voice-based experiences, supporting real-time interactions for customers and employees.
• Evaluate, integrate, and implement third-party AI platforms, APIs, and emerging technologies to accelerate innovation and business value.
• Create proof-of-concept applications to assess new use cases such as document processing, classification, summarization, knowledge management, and workflow automation.
• Optimize prompts, model configurations, and orchestration frameworks to improve performance, accuracy, and cost efficiency.
• Partner closely with data, platform, and MLOps teams to ensure robust deployment processes, monitoring, observability, and governance practices.
• Collaborate with product managers, engineers, and business stakeholders to translate user needs into scalable AI solutions.
• Contribute to architectural discussions and establish best practices for enterprise AI adoption and integration.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related technical field.
• 6+ years of experience in AI/ML engineering, software engineering, data engineering, or natural language processing, including recent hands-on work with Generative AI and LLM-based applications.
• Demonstrated success building and deploying production-grade AI chatbots, assistants, or conversational AI solutions within the last two years.

Required Skills

• Advanced Python programming skills and experience with modern AI frameworks and libraries such as LangChain, Hugging Face, OpenAI, Transformers, or similar technologies.
• Experience working with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or equivalent platforms.
• Strong understanding of RAG architectures, prompt engineering, model evaluation, API orchestration, and LLM optimization techniques.
• Solid SQL skills and experience working with cloud-based data platforms and large-scale datasets.
• Ability to operate effectively in cross-functional teams and collaborate with product, engineering, analytics, and external partners.
• Strong communication skills with the ability to explain complex AI concepts and outcomes to both technical and non-technical audiences.

Preferred Qualifications

• Experience working in highly regulated industries such as financial services, healthcare, insurance, or telecommunications.
• Hands-on experience developing voice AI, speech-enabled assistants, or conversational systems integrated with telephony platforms.
• Familiarity with modern data stack technologies, including cloud data warehouses, transformation tools, and data orchestration frameworks.
• Experience integrating AI solutions with CRM, customer service, or workflow management platforms.
• Exposure to document intelligence use cases including OCR, information extraction, summarization, semantic search, and personalized recommendations.
• Experience supporting customer-facing applications, contact center technologies, or enterprise-scale service operations.

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Principal Machine Learning Engineer – ML Platform

REMOTE – US

$180,000-$230,000 + Equity

We’re partnered with a scaling AI‑driven technology company building large‑scale, production‑grade ML systems used in real‑time decisioning. They’re seeking a Senior Machine Learning Engineer who loves production ML over research, with an emphasis on distributed compute, reliability, and end‑to‑end ownership.

This is a role for someone who enjoys shaping platform architecture while still being hands‑on.

What you’ll be doing

• Designing, training, and deploying high‑scale ML models used in live systems
• Building distributed training pipelines (PyTorch, Ray)
• Owning the ML lifecycle across feature engineering, training, evaluation, inference, monitoring
• Improving ML reliability, observability, and reproducibility
• Working closely with engineering, SRE, and product to shape platform direction
• Contributing to ML architecture standards, CI/CD, and testing frameworks

What they’re looking for

• Strong experience delivering production ML systems end‑to‑end
• Expertise with Python, PyTorch, distributed compute (Ray, Spark)
• Background in large‑scale data processing and MLOps tooling
• Ability to diagnose production issues and drive architectural improvements
• Experience with event‑driven ML and model deployment frameworks is a plus

Why this role

• High ownership of ML platform direction
• Complex, real‑world ML workloads at meaningful scale
• Opportunity to improve and evolve existing infrastructure
• Equity + strong compensation package

HOW TO APPLY

If you’re passionate about building robust, scalable ML systems that drive real business impact, apply now.

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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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Senior Machine Learning Engineer
Fully Remote, United States | $200,000 to $230,000 base plus equity and benefits

This is a high impact opportunity to shape how Vision Language Models are applied in real world, production grade systems. You will play a central role in building and scaling VLM driven intelligence that connects edge devices with cloud based reasoning, influencing both technical direction and product outcomes in a fast moving environment.

The Company
They are a technology led organisation building intelligent, connected solutions that blend advanced machine learning with real world operational workflows. Their platform is designed to turn complex visual data into actionable insights at scale. The team brings together expertise across machine learning, computer vision, embedded systems and data science, with a strong focus on building systems that perform reliably in production.

The Role

• Own the design and development of cloud based Vision Language Model pipelines used for large scale labelling and annotation
• Drive decisions on what visual signals, metadata and contextual information are captured from edge devices to maximise VLM performance
• Build and evaluate end to end inference systems, including prompt design, fine tuning strategies and model benchmarking
• Partner closely with embedded systems, computer vision and data science teams to close the loop between devices and cloud intelligence
• Stay at the forefront of the VLM landscape, assessing and integrating commercial and open source models as capabilities evolve

Your Skills and Experience

• Strong commercial experience as a machine learning engineer working with computer vision, LLMs or Vision Language Models
• Hands on experience evaluating, fine tuning or deploying VLMs in production environments
• Deep understanding of how edge constraints such as bandwidth, latency and compute influence model design
• Proficiency with Python, PyTorch and modern cloud based inference and evaluation tooling
• Practical expertise in prompt engineering and model evaluation, with a focus on measurable performance improvements
• Comfortable owning problems end to end and collaborating across hardware and software teams

What They Offer

• Competitive base salary of $200,000 to $230,000 plus equity
• Fully remote working across the United States
• Comprehensive benefits including medical, dental and vision cover
• Flexible paid time off and a strong culture of autonomy and trust
• The opportunity to be a technical leader in the application of Vision Language Models at scale

How to Apply
If you are excited by the challenge of building production grade VLM systems with real world impact, apply now to learn more about this opportunity.

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