Lead, AI Research Engineer

Ensemble Health Partners is a leading provider of technology-enabled revenue cycle management solutions for health systems. The Lead AI Research Engineer will design and analyze machine learning experiments, develop and validate new AI techniques, and collaborate with cross-functional teams to apply research to real use cases.

Responsibilities:

• Design, execute, and analyze machine learning experiments, establishing strong baselines and selecting appropriate evaluation metrics
• Stay up to date with the latest AI research; identify, adapt, and validate novel techniques for company-specific use cases
• Define rigorous evaluation protocols, including offline metrics, user studies, and adversarial (red team) testing to ensure statistical soundness
• Specify data and annotation requirements; develop annotation guidelines and oversee quality control processes
• Collaborate closely with domain experts, product managers, and engineering teams to refine problem statements and operational constraints
• Develop reusable research assets such as datasets, modular code components, evaluation suites, and comprehensive documentation
• Work alongside ML Engineers to optimize training and inference pipelines, ensuring seamless integration into production systems
• Contribute to academic publications and represent the company in research communities, as needed

Requirements:

• 5 to 7 Years of work experience
• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred
• Candidates with a master's degree and exceptional research or industry experience will also be considered
• 5–7 years of experience in AI/ML research roles, ideally in applied or product-focused environments
• Demonstrated success in delivering research-driven solutions that have been deployed in production
• Experience collaborating in cross-functional teams across research, engineering, and product
• Strong foundational knowledge in machine learning and deep learning algorithms
• Ability to read, implement, and adapt state-of-the-art research papers to real-world use cases
• Proficiency in hypothesis-driven experimentation, ablation studies, and statistically sound evaluations
• Strong mathematical foundations in probability, linear algebra, and calculus
• Ability to translate research insights into roadmaps, technical specifications, and product improvements
• Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus
• Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO)
• Advanced programming skills in Python (preferred), C++, or Java
• Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc
• Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc