AI Engineer (REMOTE)

EnableComp LLC; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

EnableComp LLC in the United States is seeking an AI Engineer to build, train, and evaluate language models with expertise in reinforcement learning. The role emphasizes end-to-end ownership from data and experiment design to training, evaluation, and reporting.

The ideal candidate has practical RL experience, strong engineering discipline, and the ability to collaborate with domain experts while communicating results to both technical and non-technical audiences.

Qualifications

• Bachelor’s degree in computer science, ML, statistics, or related field.
• 3+ years of hands-on experience training or fine-tuning language models.

Responsibilities

• Collaborate with domain experts to capture knowledge and translate it into structured formats.
• Analyze datasets for quality and coverage, coordinating with stakeholders.
• Perform exploratory and statistical analyses to guide modeling decisions.
• Create and curate training and evaluation datasets with automated and human validation.
• Design RL environments, reward functions, and verifiers.
• Train and fine-tune language models using supervised and RL methods such as PPO, DPO, GRPO.
• Manage experiments with versioned configs, tracking, and budgets.
• Document results and present findings to engineers and business leaders.

Skills

Reinforcement learning

Language models

ML engineering

Experimentation

Data analysis

PPO

DPO

GRPO

Education

Bachelor's degree in Computer Science or related field

MS or PhD preferred

Job description

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, iterativeintelligence 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.
• 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....