AI Engineer
Build and productionize LLM systems for clinical documentation at a healthcare AI startup. Requires 7+ years experience training, fine-tuning, and evaluating models with strong focus on evals and reliability.
What You'll Do
- Train and fine-tune open source models, leveraging our vast proprietary dataset to push accuracy beyond what off-the-shelf models can do.
- Build eval datasets and pipelines that let us measure model accuracy rigorously and improve it continuously.
- Design how we measure accuracy in the first place: the metrics, harnesses, and feedback loops that turn real clinical outcomes into measurable model improvements.
- Build scalable, cost-efficient inference infrastructure with great monitoring and observability.
- Build better agentic infrastructure and partner on the interfaces that turn model capability into a great clinician experience.
- Stay at the frontier: keep up with the latest research, frontier model capabilities, and open source frameworks, and bring the best of it into production.
- Prototype quickly, then harden into scalable, secure, and reliable production systems.
You Might Be a Good Fit If You Have
- 7+ years of professional software engineering experience, with meaningful depth in AI/ML.
- Experience training, fine-tuning, or evaluating LLMs and open source models, with real opinions about what works and what does not.
- Shipped real AI software to real users. You can describe something you built, what broke, and how you fixed it.
- Strong instincts for evals, observability, and the feedback loops that turn user feedback into measurable improvement.
- Experience building agentic systems: tool use, generator and critic loops, planners and executors, and orchestration where one agent's output drives another's work.
- Comfort building infrastructure that is fast, reliable, and cost-efficient at scale.
- Startup experience shipping real features in high-growth environments.
- A product mindset, comfort across the stack, and the ability to operate in ambiguity without a clean spec.
- High standards for reliability and accuracy when real clinicians and patients depend on your work.
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