Senior AI Engineer

🔥 10 minutes ago

🎸 Tennessee – Remote

🎸 Tennessee – Remote

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 10%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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EnableComp

WebsiteLinkedInAll Job Openings

501 - 1000 employees

💼 Consulting

🛡️ Insurance

📦 Logistics

💰 Venture Round on 2022-07

Consulting • Insurance • Logistics

EnableComp is a company that specializes in revenue cycle management (RCM) for healthcare providers and health systems across the United States. They focus on processing complex claims related to Veterans Administration, Workers' Compensation, Motor Vehicle Accidents, and Out-of-State Medicaid, as well as denial management for all payer classes. By leveraging intelligent automation and their proprietary E360 RCM™ platform, EnableComp helps healthcare providers increase revenue, control costs, and streamline billing processes. With expertise in handling complex claims, the company ensures accurate and timely reimbursement, providing significant uplift to collections and improving the financial performance of its clients.

📋 Description

• Build, train, and evaluate machine learning models • Capture specialized knowledge from domain experts and represent it in structured, usable form • Analyze source material and datasets for quality, consistency, and coverage, resolving issues with stakeholders • Perform exploratory and statistical analysis to inform data, modeling, and evaluation decisions • Create and curate training and evaluation datasets using automated and human validation • Design reinforcement learning environments, reward functions, and verifiers • Train and fine-tune language models using supervised and reinforcement learning methods including PPO, DPO, and GRPO • Establish baselines, run ablations, and diagnose reward hacking, instability, and data leakage • Follow research literature and apply promising techniques • Manage reproducible experiments with versioned configurations, 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 technical write-ups • Present findings to engineers, domain experts, and business leaders • Own experiments end to end as a 100% hands-on individual contributor • Collaborate closely with domain experts and adapt data, training, and evaluation approaches as knowledge evolves

🎯 Requirements

• 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 • Experience designing evaluations for language models, including held-out sets and automated grading • Ability to analyze complex, detailed documentation and translate it into precise, structured, testable form • Clear technical writing, with the ability to explain an experiment and its results concisely • Comfort working directly with domain experts who are not machine learning practitioners • Experience working with healthcare data or in another highly regulated industry is highly preferred • Experience training models to produce structured or schema-constrained output is highly preferred • Experience with a broad range of reinforcement learning algorithms and advanced reward design is highly preferred • Experience with knowledge representation approaches such as knowledge graphs or ontologies is highly preferred • Experience generating and validating synthetic data for model training is highly preferred • Publications or open-source contributions in reinforcement learning, language model fine-tuning, or model evaluation is highly preferred • Experience handling sensitive data, including protected health information (PHI) under HIPAA is highly preferred • Timely and regular attendance • Equivalent combination of education and experience will be considered • Ability to perform each essential duty satisfactorily; reasonable accommodation may be made for qualified individuals with disabilities • Practices and adheres to EnableComp’s Core Values, Vision and Mission • Compliance with HIPAA and EnableComp data policies

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