Senior AI Engineer (REMOTE)

EnableComp is hiring a Senior AI Engineer to build, train, and evaluate machine learning models, with a focus on reinforcement learning, knowledge engineering, data analysis, and evaluation. The role owns experiments end to end and collaborates closely with domain experts to produce reliable, clearly communicated results.

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

Requirements:

• Bachelor's degree in computer science, machine learning, statistics, or a related field
• 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
• Timely and regular attendance
• Equivalent combination of education and experience will be considered
• MS or PhD
• Experience working with healthcare data or in another highly regulated industry
• Experience training models to produce structured or schema-constrained output
• Experience with a broad range of reinforcement learning algorithms and advanced reward design
• Experience with knowledge representation approaches such as knowledge graphs or ontologies
• Experience generating and validating synthetic data for model training
• Publications or open-source contributions in reinforcement learning, language model fine-tuning, or model evaluation
• Experience handling sensitive data, including protected health information (PHI) under HIPAA