Hippocratic AI is developing the first safety-focused Large Language Model (LLM) for healthcare. Our mission is to dramatically improve healthcare accessibility and outcomes by bringing deep healthcare expertise to every person. No other technology has the potential for this level of global impact on health.
Innovative mission: We are creating a safe, healthcare-focused LLM that can transform health outcomes on a global scale.
Visionary leadership: Hippocratic AI was co-founded by CEO Munjal Shah alongside physicians, hospital administrators, healthcare professionals, and AI researchers from top institutions including El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft and NVIDIA.
Strategic investors: We have raised a total of $278 million in funding, backed by top investors such as Andreessen Horowitz, General Catalyst, Kleiner Perkins, NVIDIA’s NVentures, Premji Invest, SV Angel, and six health systems.
Team and expertise: We are working with top experts in healthcare and artificial intelligence to ensure the safety and efficacy of our technology.
For more information, visit .
We value in-person teamwork and believe the best ideas happen together. Our team is expected to be in the office five days a week in Palo Alto, CA unless explicitly noted otherwise in the job description.
We are seeking a Machine Learning Infrastructure Engineer to design, build, and manage the next-generation training and inference platform for LLMs. You will be at the heart of building scalable, efficient infrastructure that supports our researchers and engineers in training, serving, and experimenting with large models at scale. Your work will directly impact our ability to innovate with new architectures and training techniques in production environments.
LLM Training Infrastructure: Design and operate large-scale training clusters using Kubernetes and/or Slurm for LLM experimentation, fine-tuning, and RLHF workflows.
Cluster & GPU Management: Own scheduling, autoscaling, resource allocation, and monitoring across high-performance GPU clusters (NVIDIA, AMD).
Distributed Systems: Build and optimize distributed data pipelines using frameworks like Ray , enabling parallel training and inference jobs.
Inference Optimization: Benchmark and optimize model serving performance with technologies like vLLM , and support autoscaling of inference workloads in production environments.
Platform Reliability: Collaborate with infra and platform engineers to ensure system robustness, observability, and maintainability of ML workloads.
Research Enablement: Partner closely with ML researchers to enable rapid experimentation through flexible and efficient infrastructure tooling.
5+ years of experience in infrastructure, MLOps, or systems engineering, ideally with time spent in architect or staff-level roles.
Proven experience managing large-scale Kubernetes or Slurm clusters for training or serving ML workloads.
Strong proficiency in Python ; familiarity with Go or Rust is a plus.
Hands-on experience with Ray , vLLM , Hugging Face Transformers , and/or custom LLM training stacks.
Deep understanding of GPU scheduling , container orchestration, and workload optimization across heterogeneous hardware.
Experience with inference workloads , benchmarking, latency optimization, and cost-performance tradeoffs.
Familiarity with Reinforcement Learning, particularly RLHF frameworks, is a strong plus.
Contributions to internal platforms that enabled others to train or fine-tune LLMs efficiently.
Exposure to multiple hardware platforms (e.g., H100s, A100s, MI300X).
Experience with managing storage, IOPS performance, and object store integration for ML data.
Familiarity with building observability into ML pipelines (e.g., Prometheus, Grafana, Datadog).
Ability to present infra systems/platforms to technical stakeholders.
***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. If anything appears suspicious, stop engaging immediately and report the incident.
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