Senior AI Engineer (REMOTE)
About The Position
The Senior AI Engineer is a hands-on individual contributor who builds, trains, and evaluates machine learning models. The role combines knowledge engineering and data analysis with applied reinforcement learning, owning work end to end from data and experiment design through training, evaluation, and reporting. The engineer is expected to bring practical reinforcement learning experience, strong engineering discipline, and the ability to collaborate closely with domain experts and communicate results to technical and non-technical audiences. This is a 100% hands-on individual contributor role. Roughly half of the work focuses on reinforcement learning and model training, with the remainder split between knowledge engineering, data analysis, and evaluation. Success requires sustained collaboration with domain experts and a working understanding of the subject matter. Because the underlying knowledge evolves over time, the engineer must design data, training, and evaluation approaches that adapt without starting over. All work must comply with HIPAA and EnableComp data policies, using de-identified or synthetic data wherever possible.
Requirements
- Bachelor’s degree in computer science, machine learning, statistics, or a related field, or equivalent hands-on experience.
- At least 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 language-model policy optimization methods 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 language-model evaluations, including held-out sets and automated grading.
- Ability to analyze complex, detailed documentation and translate it into precise, structured, testable form.
- Clear technical writing skills, including the ability to explain an experiment and its results concisely.
- Comfort working directly with domain experts who are not machine learning practitioners.
- Ability to perform each essential duty satisfactorily; reasonable accommodation may be made for qualified individuals with disabilities.
- Adhere to EnableComp’s Core Values, Vision, and Mission.
Nice To Haves
- Master’s or PhD degree.
- 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.
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 appropriate 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 such as PPO, DPO, and GRPO.
- Establish baselines, run ablations, and diagnose training issues such as reward hacking, instability, and data leakage.
- Follow 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.
- Maintain timely and regular attendance.
- Comply with HIPAA and EnableComp data policies, using de-identified or synthetic data wherever possible.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
