AI Engineer

🕒 Ontem

🎸 Tennessee – Remoto

🎸 Tennessee – Remote

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🤖 Engenheiro de IA

👻 Score fantasma 10%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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EnableComp

SiteLinkedInTodas as Vagas

501 - 1000 funcionários

💼 Consultoria

🛡️ Seguros

📦 Logística

💰 Venture Round em 2022-07

Consulting • Insurance • Logistics

A EnableComp é uma empresa especializada em gerenciamento de ciclo de receita (RCM) para prestadores de saúde e sistemas de saúde em todo os Estados Unidos. Eles se concentram no processamento de sinistros complexos relacionados à Administração de Veteranos, Compensação de Trabalhadores, Acidentes de Veículos Motorizados e Medicaid fora do estado, bem como na gestão de recusas para todas as classes de pagadores. Ao alavancar a automação inteligente e sua plataforma proprietária E360 RCM™, a EnableComp ajuda os prestadores de serviços de saúde a aumentar a receita, controlar custos e simplificar os processos de faturamento. Com expertise no tratamento de sinistros complexos, a empresa garante reembolsos precisos e oportunos, proporcionando um aumento significativo nas cobranças e melhorando o desempenho financeiro de seus clientes.

Descrição

• Work with domain experts to capture specialized knowledge in structured, usable form • Analyze source materials and datasets for quality, consistency, and coverage • 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 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 technical write-ups • Present findings to engineers, domain experts, and business leaders • Own experiments from design through code, training, and results • Collaborate closely with domain experts and adapt data, training, and evaluation approaches as knowledge evolves • Ensure all work complies with HIPAA and EnableComp data policies

🎯 Requisitos

• 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 • Reinforcement learning experience, including designing an environment and reward function end to end and training a policy • Working knowledge of PPO, DPO, or GRPO and their failure modes • Strong Python and PyTorch skills • 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 language-model evaluations, including held-out sets and automated grading • Ability to analyze complex documentation and translate it into precise, structured, testable form • Clear technical writing • Comfort working with non-machine-learning domain experts • Timely and regular attendance • Equivalent combination of education and experience will be considered • Compliance with HIPAA and EnableComp data policies • Experience with healthcare data or another highly regulated industry highly preferred • Experience with structured or schema-constrained model output highly preferred • Broad reinforcement learning algorithms and advanced reward design experience highly preferred • Knowledge graphs or ontologies experience highly preferred • Synthetic data generation and validation experience highly preferred • Publications or open-source contributions in relevant fields highly preferred • Experience handling PHI under HIPAA highly preferred

🏖️ Benefícios

• Full-time employment • Professional growth and development opportunities • Tools, resources, and support for career growth • Equal opportunity, discrimination-free workplace • Reasonable accommodation for qualified individuals with disabilities • Remote work arrangement