Note: The job is a remote job and is open to candidates in USA. Truemed is hiring a Senior ML Engineer to improve its machine learning system for classifying products against HSA/FSA eligibility rules. The role focuses on designing and evaluating models, running experiments, shipping improvements to production, and collaborating with clinical operations and legal teams.
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
Skills
- 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
- Experience with healthcare or regulatory classification, or multi-model orchestration
Benefits
- Comp and equity in the top decile for Series A startups
- 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
- 2x/year team offsites
Company Overview