AI Engineer (REMOTE)

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 We are seeking a forward-thinking and deeply technical AI Security Architect to lead our efforts in securely harnessing the power of Artificial Intelligence. As the AI Security Architect, you will be responsible for designing, implementing, and managing the security posture for all AI and Machine Learning initiatives across the organization. This is a pivotal role designed for an expert who is passionate about building secure AI frameworks from the ground up. You will be the go-to authority for all matters related to AI security, working across departments to embed security into our AI strategy and operations. 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. - 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. Requirements & Qualifications - Bachelor’s degree in computer science, machine learning, statistics, or a related field; MS or PhD preferred. Equivalent hands-on experience will be considered. - 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 policy optimization methods for language models, 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.