Senior ML Engineer

Remote: yes Posted: 2026-10-20

THE ROLE

Truemed enables health and wellness merchants to accept $150+ billion in HSA/FSA funds. At the center of that is determining whether products qualify for pretax spending - a classification problem with regulatory, financial, and clinical consequences at scale.

We solve this using cutting-edge ML technologies that enable us to scale the number of merchants and product categories we can support while maintaining industry-leading accuracy requirements. As a Senior ML Engineer, you will run the experiments that decide how that system improves, and ship the winners to production.

- 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

WHAT YOU BRING

- 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

- Comp and equity in the top decile for Series A startups. This position's pay band is $180k 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