Senior AI Engineer

Posted 3hrs ago

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Job Description

Senior AI Engineer building and evaluating reinforcement-learning language models for EnableComp. Owning experiments, datasets, and model evaluations while complying with HIPAA data policies.

Responsibilities:

Build, train, and evaluate machine learning models
Capture specialized knowledge from domain experts and represent it in structured, usable form
Analyze source material and datasets for quality, consistency, and coverage, resolving issues with stakeholders
Perform exploratory and statistical analysis to inform data, modeling, and evaluation decisions
Create and curate training and evaluation datasets using automated and human validation
Design reinforcement learning environments, reward functions, and verifiers
Train and fine-tune language models using supervised and reinforcement learning methods including PPO, DPO, and GRPO
Establish baselines, run ablations, and diagnose reward hacking, instability, and data leakage
Follow research literature and apply promising techniques
Manage reproducible experiments with versioned configurations, experiment tracking, and compute budgeting
Design and extend evaluation frameworks, metrics, and benchmarks
Compare models and approaches through structured experiments and A/B testing
Document results, failures, and next steps in technical write-ups
Present findings to engineers, domain experts, and business leaders
Own experiments end to end as a 100% hands-on individual contributor
Collaborate closely with domain experts and adapt data, training, and evaluation approaches as knowledge evolves

Requirements:

Bachelor’s degree in computer science, machine learning, statistics, or a related field; MS or PhD preferred
Equivalent hands-on experience will be considered
3+ years of hands-on experience training or fine-tuning language models
Demonstrated reinforcement learning experience, including designing an environment and reward function end to end and training a policy in it
Working knowledge of policy optimization methods for language models, such as PPO, DPO, or GRPO, including their failure modes
Strong Python and PyTorch skills, with fluency in the Hugging Face ecosystem and parameter-efficient fine-tuning techniques
Experience building and evaluating LLM-based agents or multi-turn systems
Experience designing evaluations for language models, including held-out sets and automated grading
Ability to analyze complex, detailed documentation and translate it into precise, structured, testable form
Clear technical writing, with the ability to explain an experiment and its results concisely
Comfort working directly with domain experts who are not machine learning practitioners
Experience working with healthcare data or in another highly regulated industry is highly preferred
Experience training models to produce structured or schema-constrained output is highly preferred
Experience with a broad range of reinforcement learning algorithms and advanced reward design is highly preferred
Experience with knowledge representation approaches such as knowledge graphs or ontologies is highly preferred
Experience generating and validating synthetic data for model training is highly preferred
Publications or open-source contributions in reinforcement learning, language model fine-tuning, or model evaluation is highly preferred
Experience handling sensitive data, including protected health information (PHI) under HIPAA is highly preferred
Timely and regular attendance
Equivalent combination of education and experience will be considered
Ability to perform each essential duty satisfactorily; reasonable accommodation may be made for qualified individuals with disabilities
Practices and adheres to EnableComp’s Core Values, Vision and Mission
Compliance with HIPAA and EnableComp data policies

Senior AI Engineer

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