Head of Applied AI/ML Engineering

Deffai; San Francisco (CA); On-site; USD 260,000 - 340,000; Full time

Benefits offered by this job

Equity

Remote-first with in-person days

Job summary

Deffai is seeking a Head of AI/ML Engineering to own the technical direction of its FDA regulatory intelligence platform. This deeply hands-on founding role will shape ML strategy, architect AI systems, set technical standards, and grow the engineering team over time.

You will own the full ML and data pipeline including data ingestion, evaluation framework, maintenance, and post-deployment monitoring. Remote-first with SF presence, equity included.

Qualifications

• Hands-On builder who ships ML/AI systems in production
• Experience delivering end-to-end ML/AI capabilities
• Proficient in Python, PyTorch, and modern ML stack

Responsibilities

• Build & Ship: Own end-to-end delivery of AI/ML systems in production as the primary builder.
• Architecture: Drive architectural decisions balancing innovation, scalability, cost, and maintainability in a high-stakes domain.
• Product Partnership: Collaborate with founding team and FDA domain experts on product vision and differentiation.
• Own the Pipeline: Own data collection and ingestion architecture.
• Ingest at Scale: Build scalable pipelines to ingest, extract, and structure diverse regulatory sources.
• Data Quality: Ensure data provenance, versioning, and governance with automated validation and monitoring.
• Extensibility: Design ingestion to expand across new product categories and document types.

Skills

Python

PyTorch

ML pipelines

Data engineering

Cloud infra

Experimentation

Leadership

Tools

ML tooling

Job description

Approximately 20% of the US economy is governed by the FDA spanning drug, medical device, cosmetic and even pet food.

Deffai is reimagining how products are approved by the FDA with cutting-edge AI purpose-built for medical devices, drugs, and therapeutics companies. We're building the world's largest FDA regulatory AI by combining FDA regulatory expertise and data from diverse sources.

You will be working with a mission-driven and energetic team excited to build the future of FDA regulatory approval.

We're growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact on how medicines are made and approved.

About the Role

We're looking for a Head of AI/ML Engineering to own the technical direction of Deffai's FDA regulatory intelligence platform. This is a deeply hands-on founding role: You will shape our ML strategy and architect AI systems, while setting technical standards and growing the engineering team over time.

You'll own the full ML and data pipeline including data ingestion, eval framework, maintenance and post-deployment monitoring. You'll work with diverse data sources, evaluate our internal intelligence platform with benchmarks and human feedback.

If you're excited by hard technical challenges, fast iteration, and the opportunity to define how regulatory AI works at scale — while owning the codebase and building the team that makes it durable — this is a rare chance to do it from the ground up.

Remote first, San Francisco-based. The work is collaborative; expect to spend a few days a week working in person.

Competitive compensation with equity.

Key Responsibilities

• Build & Ship: Own end-to-end delivery of AI/ML systems in production as the primary builder.
• Architecture: Drive architectural decisions that balance innovation, scalability, cost, and long-term maintainability in a high-stakes domain.
• Product Partnership: Partner with the founding team and FDA domain experts on product vision, challenge assumptions, and ensure differentiation beyond general-purpose LLM capabilities.
• Own the Pipeline: Own the architecture of the data collection and ingestion pipeline.
• Ingest at Scale: Build robust, scalable pipelines to ingest, extract, and structure heterogeneous regulatory sources into curated datasets.
• Data Quality: Ensure data quality, provenance, versioning, and governance across the corpus, with automated validation and monitoring.
• Extensibility: Design ingestion to expand cleanly across new product categories and document types as the knowledge base grows.

Eval and Feedback Loops

• Benchmark & Evaluate: Build datasets, define rigorous metrics, and measure model performance across high-impact AI tasks to guide development.
• Run Human Evaluations: Build scalable pipelines to collect structured human feedback, benchmark subjective quality, and inform model iterations.
• Build the Team: Establish, grow, and manage a high-caliber ML engineering organization as the company scales.
• Mentor: Hire, mentor, and develop engineers; set expectations for accountability, ownership, and continuous growth.
• Operating Model: Establish a flat, hands-on operating model where leaders stay close to the work while empowering the team to execute independently.
• Level Up Infrastructure: Design and maintain the ML infrastructure needed for fast experimentation, robust training, and continuous deployment.
• Engineering Rigor: Drive engineering rigor across testing, release readiness, and post-launch support while maintaining enterprise-grade reliability.

Who you are

• Hands-On Builder First: You have a proven track record building and shipping ML/AI systems in production, beyond experimentation or research — and you still love to build.
• ML Engineer with Real-World Experience: You've trained and shipped models in production and can own model performance end-to-end.
• Fluent in the Modern ML Stack: You know your way around Python, PyTorch, and today's ML tools, from training pipelines to evaluation benchmarks.
• Data & Infra Depth: You have experience building data pipelines and ML infrastructure for ingestion, training, and continuous deployment.
• Startup-Ready: You're adaptable, resilient, and energized by ambiguity and fast-changing priorities.
• Execution-Oriented: You move fast, take ownership, and focus on solving real problems over perfect ones.
• Clear Communicator & Team Player: You collaborate well across functions and push decisions forward.

Preferred: Experience in legal or other regulated, high-stakes domains; a track record of building applied AI products in startup or startup-like contexts that prioritize rapid market introduction.