Senior ML Engineer
At a glance
Senior Machine Learning Engineering role at Truemed. Remote, United States · full-time · $180,000–$250,000 base.
remoteroles summary, based on the employer's posting.
What you'll do
- Test ideas against labeled product data, then deploy the experiments that raise classification accuracy
- Build product eligibility models using prompts, retrieval, fine-tuning or multiple-model methods
- Study incorrect product decisions for recurring breakdowns and use those patterns to plan further tests
- Expand model scorecards and evaluation data, including ground-truth labeling methods
- Translate eligibility rules with clinical operations and legal colleagues into labeled examples and system tests
What you bring
- At least five years in engineering or applied research, including two years using LLMs or other ML models on real tasks
- A strong experimental record, with explicit hypotheses, reliable baselines, ablations and conclusions you can support
- You can define correctness for AI systems and build a method to measure it
- Hands-on foundation-model work with prompting, fine-tuning, retrieval or agent design, plus Python skills to ship experiments
- You have independently carried an unclear ML challenge from its first test into production
Who this fits
This senior role suits someone who likes extended, hands-on data review and can discover an approach through testing. It is a full-time remote position for someone in the United States. You will own ML work through production and coordinate with clinical operations and legal colleagues.
From the employer
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.
What you'll do
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
About Truemed
Our mission is to help people take control of their health. Today, 95% of all medical costs go to treating illness. Our goal is to empower people to invest in healthy habits today rather than treating illness in the future. We do this by supporting these 5 habits that research shows are tied to significantly improved health outcomes:
Eating healthy food
Getting an adequate quantity and quality of sleep
Increasing movement and exercise and reducing sedentary behavior
Reducing chronic stress
Increasing sunlight and minimizing environmental toxins
Today Truemed enables patients to use pretax HSA/FSA funds to buy healthy food, exercise equipment, and supplements. We support brands like Peloton, Eight Sleep, Momentous, and hundreds of others. We're building the infrastructure that allows health and wellness merchants to accept the $150+ billion tied up in HSA and FSA accounts. By allowing consumers to spend these pre-tax dollars on goods that make them healthier, we'll incentivize consumers to invest in their health and begin to reverse the explosion in chronic illness that's plagued the US for the last 50 years.
Compensation and Benefits
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
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