Healthcare AI Engineer - RL & LLM Specialist

EnableComp LLC; Oregon; Hybrid; USD 120,000 - 180,000; Full time

Job summary

EnableComp in Oregon seeks an AI Engineer to build, train, and evaluate machine learning models, focusing on reinforcement learning and language-model fine-tuning. You will own experiments end to end, from data and experiment design through training, evaluation, and reporting.

You will work with domain experts to capture knowledge, create training datasets, and design reinforcement learning environments with custom reward functions.

Qualifications

• Bachelor's degree in computer science, machine learning, statistics, or a related field; MS or PhD preferred.
• 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.

Responsibilities

• Work with domain experts to capture specialized knowledge and represent it in structured, usable form.
• Analyze source material and datasets for quality, consistency, and coverage, and resolve issues with the appropriate stakeholders.
• Perform exploratory and statistical analysis to inform data, modeling, and evaluation decisions.
• Create and curate training and evaluation datasets, using both automated and human validation.
• Design reinforcement learning environments, reward functions, and verifiers.
• Train and fine‑tune language models using supervised and reinforcement learning methods such as PPO, DPO, and GRPO.
• Establish baselines, run ablations, and diagnose training issues such as reward hacking, instability, and data leakage.
• Follow the research literature and bring promising techniques into practice.
• Manage experiments rigorously, including versioned configurations, reproducible runs, 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 clear technical write‑ups.
• Present findings to engineers, domain experts, and business leaders in language each audience can act on.

Skills

Python

PyTorch

Hugging Face

Reinforcement learning

Experiment design

Education

Bachelor's degree in CS/ML/Statistics

MS or PhD preferred

Tools

TensorFlow

Jupyter

Job description

EnableComp in Oregon seeks an AI Engineer to build, train, and evaluate machine learning models, focusing on reinforcement learning and language-model fine-tuning. You will own experiments end to end, from data and experiment design through training, evaluation, and reporting.

You will work with domain experts to capture knowledge, create training datasets, and design reinforcement learning environments with custom reward functions.