Lead Research Engineer

Requirements:

• 7-10+ years of hands-on experience in ML/NLP/LLM engineering, with a strong track record of leading applied production systems.
• Demonstrated experience owning technical workstreams independently, not just contributing to them.
• Expert-level Python and deep ML engineering skills; ability to write, review, and improve production-grade code across the team.
• Deep understanding of LLMs, transformers, embeddings, RAG, fine-tuning (LoRA/SFT/DPO), prompt engineering, evaluation, and structured output generation.
• Strong evaluation thinking: ability to design rigorous experiment plans, define meaningful metrics, manage hidden test sets, and reason about model failure modes across clinical edge cases.
• Experience mentoring engineers, running design reviews, and raising execution quality across a team.
• Ability to make confident architecture decisions, communicate tradeoffs clearly, and drive cross-functional alignment.
• Strong engineering discipline: versioning, testing, documentation, reproducibility, and peer review as a non-negotiable standard.
• Ability to work with imperfect clinical data, annotation disagreement, ambiguous labels, and regulated data constraints.