truemed - Senior ML Engineer
Requirements
• 5+ years of engineering or applied research experience, with at least 2 years of hands-on work with LLMs or other ML models on real problems • A track record of rigorous experiments: clear hypotheses, sound baselines, ablations, and conclusions you would defend • You thrive and enjoy looking at data with your eyes for hours and consider it part of the workflow in any ML work • Comfort designing evaluation methodology for AI systems - defining what "correct" means and measuring it • Hands-on experience with prompting, fine-tuning, retrieval, or agent design on foundation models (OpenAI, Anthropic, open-weight models) • Fluency in Python and enough engineering depth to take an experiment to production • Senior-level ownership: you've taken an ambiguous ML problem from first experiment to production on your own • Comfort with ambiguity - the right approach isn't always known upfront and has to be discovered through experimentation • Public evidence of how you run experiments: a paper, open-source ML work, or a technical write-up of results you would defend • Bonus: experience with healthcare or regulatory classification, or multi-model orchestration
Responsibilities
• Run the experiments that move classification accuracy: form a hypothesis, test it against labeled data, and ship what wins • Design and iterate on classifiers that evaluate products against eligibility rules, across prompting, retrieval, fine-tuning, and multi-model approaches • Dig into misclassifications to find failure modes, and turn each one into the next experiment • Build and extend the evaluation sets and metrics that tell us when a model is better, including how we label ground truth • Track new models and research, and test the ones that could change our accuracy or cost curve • Extend classification coverage to new product categories as Truemed expands • Find new applications for our models and data beyond product classification, and prototype the ones worth pursuing • Collaborate with clinical ops and legal teams to turn eligibility standards into labeled data and testable system behavior • Write up results so the team makes model decisions from evidence, and help raise AI fluency across engineering
Benefits
• Comp and equity in the top decile for Series A startups. This position's pay band is $205k to $250k plus stock options • Comprehensive medical, dental, and vision insurance with employer-sponsored HSA contributions • 401(k), unlimited PTO, remote work • Coworking stipend, L&D stipend • 2x/year company offsites and 2x/year team offsites
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