AI Engineer

EnableComp provides Specialty Revenue Cycle Management solutions for healthcare organizations, leveraging over 24 years of industry-leading expertise and its unified E360 RCM™ intelligent automation platform to improve financial sustainability for hospitals, health systems, and ambulatory surgery centers (ASCs) nationwide. Powered by proprietary algorithms, iterative intelligence from 10M+ processed claims, and expert human-in-the-loop integration, EnableComp provides solutions across the revenue lifecycle for Veterans Administration, Workers’ Compensation, Motor Vehicle Accidents, and Out-of-State Medicaid claims as well as denials for all payer classes. By partnering with clients to supercharge the reimbursement process, EnableComp removes the burden of payment from patients and provider organizations while enabling accelerated cash, higher and more accurate yield, clean AR management, reduced denials, and data-rich performance management. EnableComp is a multi-year recipient the Top Workplaces award and was recognized as Black Book's #1 Specialty Revenue Cycle Management Solution provider in 2024 and is among the top one percent of companies to make the Inc. 5000 list of the fastest-growing private companies in the United States for the last eleven years. 

  Position Summary  

The 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, and owns work end to end, from data and experiment design through training, evaluation, and reporting.

The ideal candidate brings 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.

Key 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.