EnableComp

EnableComp specializes in complex claims and specialty revenue cycle management solutions for healthcare providers, focusing on optimizing reimbursements for various claims types, including VA, Workers' Compensation, and Motor Vehicle Accidents, throug...

Insurance

Financials

201-500 (480)

Founded 2000

19 open positions

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Senior AI Engineer (REMOTE)

53 minutes ago

United States

Full-time

Senior

AI Engineer

Software Development

HIPAA Hugging Face LLM Machine Learning Python PyTorch Reinforcement Learning

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EnableComp

Insurance

201-500

Founded 2000

View All Jobs 19

Description

Partner with domain experts to capture specialized knowledge in structured, usable formats.
Analyze source materials and datasets for quality, consistency, and coverage, resolving issues with stakeholders.
Create and curate training and evaluation datasets using automated and human validation.
Design reinforcement learning environments, reward functions, verifiers, and policy-training workflows.
Train and fine-tune language models with supervised learning and reinforcement learning methods, including PPO, DPO, and GRPO.
Establish baselines, run ablations, diagnose training issues, and manage reproducible experiments and compute budgets.
Design and extend evaluation frameworks, metrics, benchmarks, and A/B tests to compare model approaches.
Document and present experiment results, failures, and next steps to technical and non-technical stakeholders.
Ensure data, training, and evaluation approaches adapt to evolving knowledge while complying with HIPAA and company data policies.

Requirements

Bachelor’s degree in computer science, machine learning, statistics, or a related field; equivalent hands-on experience considered; MS or PhD preferred.
3+ years of hands-on experience training or fine-tuning language models.
Demonstrated end-to-end reinforcement learning experience, including environment and reward-function design and policy training.
Knowledge of language-model policy optimization methods such as PPO, DPO, and GRPO, including failure modes.
Strong Python and PyTorch skills, including the Hugging Face ecosystem and parameter-efficient fine-tuning.
Experience building and evaluating LLM-based agents or multi-turn systems.
Experience designing language-model evaluations, including held-out datasets and automated grading.
Ability to translate complex documentation into precise, structured, testable representations and communicate experimental results clearly.
Ability to work closely with non-ML domain experts and maintain timely, regular attendance.
Experience with healthcare or other regulated data, PHI/HIPAA, structured outputs, advanced RL and reward design, knowledge graphs or ontologies, synthetic data, or relevant publications/open-source contributions is preferred.

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