AI Engineer

Job Description:

• Work with domain experts to capture specialized knowledge in structured, usable form
• Analyze source materials and datasets for quality, consistency, and coverage
• 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 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 technical write-ups
• Present findings to engineers, domain experts, and business leaders
• Own experiments from design through code, training, and results
• Collaborate closely with domain experts and adapt data, training, and evaluation approaches as knowledge evolves
• Ensure all work complies with HIPAA and EnableComp data policies

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
• Reinforcement learning experience, including designing an environment and reward function end to end and training a policy
• Working knowledge of PPO, DPO, or GRPO and their failure modes
• Strong Python and PyTorch skills
• 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 language-model evaluations, including held-out sets and automated grading
• Ability to analyze complex documentation and translate it into precise, structured, testable form
• Clear technical writing
• Comfort working with non-machine-learning domain experts
• Timely and regular attendance
• Equivalent combination of education and experience will be considered
• Compliance with HIPAA and EnableComp data policies
• Experience with healthcare data or another highly regulated industry highly preferred
• Experience with structured or schema-constrained model output highly preferred
• Broad reinforcement learning algorithms and advanced reward design experience highly preferred
• Knowledge graphs or ontologies experience highly preferred
• Synthetic data generation and validation experience highly preferred
• Publications or open-source contributions in relevant fields highly preferred
• Experience handling PHI under HIPAA highly preferred

Benefits:

• Full-time employment
• Professional growth and development opportunities
• Tools, resources, and support for career growth
• Equal opportunity, discrimination-free workplace
• Reasonable accommodation for qualified individuals with disabilities
• Remote work arrangement