Senior AI Engineer

🕒 Ontem

🎸 Tennessee – Remoto

🎸 Tennessee – Remote

⏰ Tempo Integral

🟠 Sênior

🤖 Engenheiro de IA

👻 Score fantasma 10%

O que significa: a chance de ninguém estar realmente contratando para esta vaga.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

• 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

🎯 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 • 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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