Head of AI/ML Engineering
📍 San Francisco Bay Area, CA
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About Deffai
In the near future, drug discovery will no longer be the bottleneck in life sciences: Eli Lilly's $2B+ partnership with Insilico Medicine, Anthropic's $400M+ acquisition of Coefficient Bio, and OpenAI's partnership with Novo Nordisk. Capital and resources are flowing into drug discovery, but this doesn't address the downstream bottleneck — if there is an abundance of molecules discovered, how eligible are they for regulatory approval and commercialization?
Deffai is reimagining how drugs 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.
Our team is made of passionate domain experts and experienced entrepreneurs across life sciences and software products. We're 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.
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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 leadership role: You will shape our ML strategy alongside the founding team, write production code and architect our 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.
San Francisco-based. The work is collaborative; expect to spend a few days a week working in person with our cofounder, usually at a coffee shop or coworking space.
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Key Responsibilities
▸ Technical & Product Leadership
- Build & Ship: Own end-to-end delivery of AI/ML systems in production — as the primary builder — moving beyond proofs-of-concept to scalable, reliable, customer-facing regulatory intelligence.
- 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.
▸ Data Collection & Ingestion Pipeline
- Own the Pipeline: Own the architecture of the data collection and ingestion pipeline that powers Deffai's FDA regulatory knowledge base — the company's core moat.
- Ingest at Scale: Build robust, scalable pipelines to ingest, extract, and structure heterogeneous regulatory sources into training- and retrieval-ready 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.
▸ Evaluation & Quality
- 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.
▸ Team & Organizational Leadership
- 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.
▸ Execution & Operations
- Deploy to Production: Work closely across the team to ship models, monitor them in the wild, and ensure they stay fast, reliable, and accurate at scale.
- 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.
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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.
- Emerging Leader: You've led or mentored engineers and can set technical direction and scale a team over time.
- 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 regulated, high-stakes, or life-sciences domains; a track record of building applied AI products in startup or startup-like contexts that prioritize rapid market introduction.
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Job Details
- Location: San Francisco Bay Area, CA, US
- US Visas: Deffai is open to sponsoring work authorization for qualified candidates, including H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.