Artificial Intelligence Researcher

One of our clients who are a leading provider of Revenue Cycle Management (RCM) for the healthcare industry are looking to fill "AI Research Scientist" (various levels - Senior, Lead & Staff) roles, ideally in applied or product-focused environments.

Hybrid role (3 days onsite either from San Jose, CA or Austin, TX)

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.

Educational Qualifications:

• 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.

Industry Experience:

• 3–5 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.
• Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus.

Technical Skills:

• Strong foundational knowledge in machine learning and deep learning algorithms.
• Hands-on experience with PEFT/LoRA, adapters, fine-tuning techniques, and RLHF/RLAIF (e.g., PPO, DPO, GRPO).
• 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.
• Advanced programming skills in Python (preferred), C , or Java.
• Experience with deep learning frameworks such as PyTorch, Hugging Face, NumPy, etc.
• Strong mathematical foundations in probability, linear algebra, and calculus.
• Domain expertise in one or more areas: natural language processing (NLP), symbolic reasoning, speech processing, etc.
• Ability to translate research insights into roadmaps, technical specifications, and product improvements.