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AI Engineer (REMOTE)

1 week ago
United States
Full-time
Mid Level
AI Engineer
Software Development
HIPAA Hugging Face LLM Machine Learning Python PyTorch Reinforcement Learning
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EnableComp

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
201-500
Founded 2000
View All Jobs 18

Description

  • Capture specialized domain knowledge with subject-matter experts and represent it in structured, usable formats.
  • Analyze source materials and datasets for quality, consistency, and coverage; resolve 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 approaches.
  • Train and fine-tune language models using supervised learning and reinforcement learning methods, including PPO, DPO, and GRPO.
  • Establish baselines, conduct ablations, diagnose training issues, and compare approaches through structured experiments and A/B testing.
  • Design and extend model evaluation frameworks, metrics, benchmarks, and automated grading methods.
  • Manage reproducible experiments through versioned configurations, tracking, compute budgeting, and technical documentation.
  • Present experiment findings, failures, and recommendations to engineering, domain, and business audiences.
  • Ensure work complies with HIPAA and company data policies, using de-identified or synthetic data where possible.

Requirements

  • Bachelor’s degree in computer science, machine learning, statistics, or a related field; MS or PhD preferred, with equivalent hands-on experience considered.
  • 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.
  • Working 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 techniques.
  • 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 clearly document experiment results.
  • Ability to collaborate closely with non-ML domain experts and communicate findings to technical and non-technical audiences.
  • Healthcare or other regulated-industry experience; HIPAA/PHI handling, structured-output training, advanced reward design, knowledge graphs or ontologies, synthetic data validation, and relevant publications or open-source contributions are preferred.

Benefits

  • Professional growth and career-development support.
  • Tools, resources, and support to help employees develop and succeed.
  • Collaborative, family-oriented culture focused on shared goals and employee empowerment.
  • Equal-opportunity workplace with reasonable accommodations for qualified individuals with disabilities.

Interested in this position?

Apply directly on the company website

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