Applied Scientist, Reinforcement Learning (Mid, Senior, Staff)

As HAI's LLM Post-Training Applied Scientist, you will own the reinforcement learning and on-policy distillation pipeline that transforms raw model capability into reliable, safe clinical behavior. Your post-training methods will directly determine how our AI agents reason through complex clinical scenarios, handle safety-critical decisions, and ultimately impact millions of patient interactions. This role exists because post-training is where capability becomes trustworthiness—and in healthcare, that's everything.

Own your first major outcome: By day 90, you will have shipped a post-training improvement that meaningfully advances model performance on a critical clinical capability (clinical reasoning, safety alignment, or task completion), evaluated the gains rigorously, and contributed that learning to our post-training roadmap.

Drive lasting impact: At 12 months, you will have designed and shipped multiple post-training methods that measurably improve our models' clinical safety and reasoning, built reusable infrastructure (reward models, verifiers, evaluation frameworks) that accelerate future post-training work, published your research or contributed to HAI's intellectual property, and directly shaped how our deployed models behave in production healthcare environments.

You'll work alongside ML researchers, engineers, clinicians, and safety experts who are obsessed with building trustworthy AI. This is a highly technical team that values rigor, collaboration across disciplines, and solving the hardest problems in AI safety and alignment. You'll have direct influence on model architecture and training decisions.

• Design and implement RL and OPD post-training methods including RLHF, RLVR, on-policy distillation, and novel approaches tailored to healthcare AI—selecting the right methods for different clinical reasoning and safety challenges

• Build and evaluate reward models, verifiers, and LLM-as-judge pipelines that provide reliable training signals for post-training, ensuring they capture what truly matters in clinical contexts (accuracy, safety, patient experience)

• Develop conversational AI environments and simulations for healthcare RL training—creating synthetic clinical scenarios and datasets that enable safe, scalable post-training without relying solely on human feedback

• Automate post-training research loops using agents and tooling to systematically explore hyperparameters, methods, and data strategies—turning post-training into a scientific, reproducible process

• Run rigorous experiments and analysis to understand what drives post-training gains, isolate the contributions of different components, and build intuition about what works in healthcare contexts

• Collaborate with research, engineering, and clinical teams to translate clinical requirements into post-training objectives, validate improvements against real-world metrics, and scale successful methods to production

We believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Menlo Park office five days a week, unless otherwise specified.

Required Qualifications

• Master's degree in Computer Science, Machine Learning, or a related field

• 5+ years of professional experience in NLP, LLM training, or reinforcement learning

• 2+ years of hands-on experience with RL for LLM post-training

• Proficiency in Python and PyTorch for large-scale training

• Demonstrated experience with RLHF, RLVR, LLM-as-judge, or similar post-training methods

• Experience training or fine-tuning models at scale (50B+ parameters)

Preferred Qualifications

• Publications at top-tier ML venues (NeurIPS, ICML, ICLR, ACL, EMNLP)

• Healthcare or regulated domain experience

• Experience with distributed training frameworks (FSDP, DeepSpeed, vLLM)

• Familiarity with safety alignment and interpretability research

Our comprehensive compensation package is designed to reward your expertise and includes both a competitive base salary and valuable stock options. Individual offers are determined based on a variety of factors, including your professional experience, core competencies, and geographic location.

Reinvent healthcare with AI that puts safety first. We’re building the world’s first healthcare‑only, safety‑focused LLM — a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation.

Work with the people shaping the future. Hippocratic AI was co‑founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA.

Backed by the world’s leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children’s, WellSpan Health, John Doerr, Rick Klausner, and others.

Build alongside the best in healthcare and AI. Join experts who’ve spent their careers improving care, advancing science, and building world‑changing technologies — ensuring our platform is powerful, trusted, and truly transformative.

Hippocratic AI is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity or expression, genetic information, military or veteran status, or any other characteristic protected by applicable law. We are committed to building a team that reflects the patients we serve. We actively encourage applications from candidates of all backgrounds. If you require accommodations during the hiring process, please contact [email protected].

Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @hippocraticai.com email addresses. We will never request payment or sensitive personal information during the hiring process.