Staff Machine Learning Engineer – Frontier AI / Clinical Intelligence
AI Healthcare Startup | Real-World Clinical AI | Hybrid (San Francisco)
We’re hiring a Staff ML Engineer (Frontier AI) to join a leading healthcare AI company building intelligent systems that improve clinical workflows at scale.
Their platform powers real-time documentation, coding, and decision support across major health systems — operating in complex environments with messy EHR data, strict compliance constraints, and high accuracy + low latency requirements.
You’ll own the hardest model quality problems and drive research that directly impacts real-world clinical outcomes.
You’ll work on:
- Clinical AI models (coding, scribing, chart understanding)
- Learning loops from real-world feedback (clinician edits, audits)
- Long-context reasoning across patient records
- Retrieval, grounding, and clinical QA systems
- Optimisation across latency, cost, and performance
What You’ll Do:
- Lead model research and drive architecture decisions
- Identify failure modes and ship end-to-end improvements
- Build systems that continuously improve from real-world data
- Apply techniques like RLHF, distillation, and model optimisation
- Collaborate across engineering, product, and domain teams
What We’re Looking For:
- 5+ years in ML engineering or applied research
- Deep expertise in RL and deep learning
- Experience taking models from research → production
- Strong Python + PyTorch experience
- Track record of improving model performance in production
Nice to have:
- Top-tier ML publications
- Experience with clinical or regulated data
- Background in RAG, long-context models, or reasoning systems
This Role Is:
- Hybrid — San Francisco (3 days onsite)
- $250K–$350K base + equity
