Senior ML Engineer
At a glance
Senior Machine Learning Engineering role at Truemed. Remote, United States · full-time · $205,000–$250,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Test hypotheses on labeled product data, then deploy the experiments that improve eligibility decisions
- Develop product classifiers with prompting, retrieval, fine-tuning, or multiple-model methods
- Inspect incorrect classifications for recurring failure patterns and use them to set up further tests
- Build evaluation examples and measures, including ground-truth labels, to compare model performance
- Work with clinical operations and legal teams to make eligibility standards measurable in system behavior
What you bring
- At least five years in engineering or applied research, including two years working directly with language models or other ML systems
- A strong experimentation record, with clear hypotheses, reliable baselines, ablation tests, and conclusions you can defend
- Experience setting evaluation methods for AI systems and measuring whether outputs are correct
- Practical foundation-model experience with prompting, fine-tuning, retrieval, or agent design, along with Python fluency
- You have taken an unclear ML challenge from an initial test through deployment on your own
Who this fits
This senior role suits an engineer who likes spending time closely examining data and finding answers through experiments. The position is remote within the United States, with ownership from early testing through production.
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 $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