Machine Learning Engineer
Owns end-to-end production ML systems for clinical workflows, including training/fine-tuning LLMs for medical reasoning and question answering. Requires strong ML/software engineering, PyTorch experience, and ability to handle high-stakes ambiguity with real patient impact.
About the job
What You’ll Do
- Own end-to-end ML systems, including architecture, data, modeling, evaluation, and production infrastructure
- Train and fine-tune large language models (LLMs) for:
- Clinical reasoning
- Medical question answering
- Evidence-grounded generation
- Make and own tradeoffs across accuracy, latency, cost, and safety in high-stakes production environments
- Develop evaluation frameworks to ensure model safety and clinical validity
- Integrate ML systems into product workflows and patient-facing applications
- Monitor system performance in production and iterate based on real-world usage and feedback
- Define what “correct” means in ambiguous clinical workflows in collaboration with engineers and clinicians
What We’re Looking For
- Strong foundation in machine learning and software engineering
- Track record of building and owning ML systems in production where performance, reliability, or correctness materially mattered
- Experience driving ambiguous ML problems from 0→1, including problem formulation, model design, and productionization
- Hands-on experience with PyTorch or similar frameworks
- Ability to operate independently in high-ambiguity environments with minimal guidance
- Strong product and engineering judgment — you know when to use ML, when not to, and how to scope problems accordingly
- Comfort working in a fast-moving, early-stage environment
- Experience working on systems where decisions have real-world consequences (e.g., healthcare, finance, infrastructure)
Nice to Have
- Experience deploying LLMs in production environments
- Experience building distributed systems or large-scale data pipelines
- Experience working with clinical, biomedical, or other regulated datasets
Compensation
Base salary: $225,000 – $300,000+ Meaningful equity in an early-stage, Series A company
Skills
PyTorch, LLMs, Reinforcement Learning, Machine Learning, Llm Fine-Tuning, Distributed Systems, Data Pipelines, Clinical Data, Production Ml, Evaluation Frameworks
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