Senior ML Engineer

Truemed

. Run experiments to improve classification accuracy and ship successful approaches to production .

Posted 10/21/2026full-timeRemote • United StatesSenior💰 $205,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and executing rigorous experiments to enhance classification accuracy, with a strong focus on applying machine learning models in production environments. Proficient in collaborating with cross-functional teams to translate complex eligibility standards into actionable data-driven solutions.

Highest-signal resume keywords

5+ Years Engineering ExperienceHands-On Experience With LLMsFluency In PythonEvaluation Methodology DesignHealthcare Classification Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Classification Accuracy ImprovementExperiment DesignModel Fine-TuningPrompting TechniquesRetrieval ApproachesMulti-Model OrchestrationGround-Truth LabelingEvaluation Metrics DevelopmentData AnalysisProduction Deployment

Soft Skills

CollaborationProblem-SolvingCommunication

Industry Keywords

Healthcare ClassificationRegulatory ComplianceMachine Learning ModelsAI SystemsExperimental Research

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Run experiments to improve classification accuracy and ship successful approaches to production
Design and iterate on classifiers evaluating products against eligibility rules using prompting, retrieval, fine-tuning, and multi-model approaches
Analyze misclassifications, identify failure modes, and define subsequent experiments
Build and extend evaluation sets and metrics, including ground-truth labeling
Track new models and research and test options that could improve accuracy or costs
Extend classification coverage to new product categories
Identify and prototype new applications for models and data beyond product classification
Collaborate with clinical operations and legal teams to translate eligibility standards into labeled data and testable system behavior
Document results and support AI fluency across engineering

Requirements

What you’ll need

5+ years of engineering or applied research experience
At least 2 years of hands-on work with LLMs or other ML models on real problems
Track record of rigorous experiments, including clear hypotheses, sound baselines, ablations, and defensible conclusions
Comfort designing evaluation methodology for AI systems
Hands-on experience with prompting, fine-tuning, retrieval, or agent design on foundation models
Fluency in Python
Engineering depth to take an experiment to production
Senior-level ownership of an ambiguous ML problem from first experiment to production
Public evidence of running experiments, such as a paper, open-source ML work, or technical write-up
Experience with healthcare or regulatory classification, or multi-model orchestration is a bonus

Benefits

Comp & perks

Stock options
Comprehensive medical, dental, and vision insurance
Employer-sponsored HSA contributions
401(k)
Unlimited PTO
Remote work
Coworking stipend
L&D stipend
2x/year company offsites
2x/year team offsites