AI Engineer (REMOTE)

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About This Role

EnableComp is seeking a remote AI Engineer to join the organization as a hands-on individual contributor. The employer delivers specialty revenue cycle management solutions to healthcare organizations nationwide, utilizing intelligent automation platforms and proprietary algorithms to manage claims and denials. In this position, approximately half of the time is dedicated to reinforcement learning and model training, while the remainder involves knowledge engineering, data analysis, and evaluation. The successful candidate will…

Job Description

Responsibilities

• Collaborate with domain specialists to gather and structure expert knowledge.
• Examine source datasets and materials to guarantee consistency, coverage, and quality.
• Conduct statistical and exploratory analysis to guide modeling and evaluation choices.
• Build and curate training datasets utilizing automated and human validation methods.
• Establish reinforcement learning verifiers, reward functions, and environments.
• Fine-tune and train language models using methods such as PPO, DPO, and GRPO.
• Track experiments meticulously with versioned configurations and reproducible runs.
• Create and expand benchmarking frameworks, metrics, and evaluation tools.
• Write technical documentation covering results, failures, and subsequent steps.
• Present findings clearly to both business leaders and technical teams.

Requirements

• Three or more years of hands-on experience training or fine-tuning language models.
• Demonstrated practical reinforcement learning background, including building an environment and reward function.
• Working familiarity with language model policy optimization techniques such as PPO, DPO, and GRPO.
• Proficiency in Python and PyTorch, along with Hugging Face ecosystem fluency.
• Background in constructing and assessing multi-turn systems or LLM-based agents.
• Ability to analyze intricate documentation and convert it into structured forms.

Qualifications

• Bachelor degree in computer science, statistics, machine learning, or a related discipline.
• Master degree or PhD in a relevant field.
• Prior work history involving healthcare data or other heavily regulated sectors.
• Experience training models for schema-constrained or structured output generation.
• Familiarity with ontologies or knowledge graphs for knowledge representation.
• Background in generating and validating synthetic training data.
• Open-source contributions or publications in language model fine-tuning or reinforcement learning.
• Experience handling protected health information and sensitive data under HIPAA.

Core Skills

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