SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 205,000 - 250,000 / annual
Truemed enables health and wellness merchants to accept $150+ billion in HSA/FSA funds by solving the core problem of determining whether products qualify for pretax spending—a classification challenge with regulatory, financial, and clinical consequences at scale.
As a Senior ML Engineer, you will own the experimentation and deployment of ML systems that improve product classification accuracy while scaling merchant and category coverage. You'll run rigorous experiments to test hypotheses against labeled data, design and iterate on classifiers using prompting, retrieval, fine-tuning, and multi-model approaches, and ship winning solutions to production.
Key responsibilities include:
- Running experiments to move classification accuracy: forming hypotheses, testing against labeled data, and shipping winners
- Designing classifiers that evaluate products against eligibility rules across multiple ML approaches
- Analyzing misclassifications to identify failure modes and convert them into new experiments
- Building and extending evaluation sets, metrics, and ground-truth labeling methodologies
- Tracking new models and research to test approaches that could improve accuracy or reduce costs
- Expanding classification coverage to new product categories as the company scales
- Exploring new applications for models and data beyond product classification
- Collaborating with clinical ops and legal teams to translate eligibility standards into labeled data and testable system behavior
- Documenting results to drive evidence-based model decisions and raise AI fluency across the engineering team
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
- Track record of rigorous experiments: clear hypotheses, sound baselines, ablations, and defensible conclusions
- Comfort with exploratory data analysis and viewing data as core to ML workflow
- Experience designing evaluation methodology for AI systems, including defining correctness 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 sufficient engineering depth to take experiments to production
- Senior-level ownership: demonstrated ability to take ambiguous ML problems from first experiment to production independently
- Comfort with ambiguity and discovering the right approach through experimentation
- Public evidence of experimental rigor: a paper, open-source ML work, or technical write-up of results you would defend
- Bonus: experience with healthcare or regulatory classification, or multi-model orchestration
