Staff Machine Learning Engineer, Applied Science

About Us

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.

Why Join Our Team

• 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.

Overview

Staff Machine Learning Engineers at Hippocratic AI are foundational to the design, deployment, and optimization of cutting-edge ML systems powering our next-generation, safety-focused generative AI for healthcare. You’ll work closely with research scientists and product teams to build scalable infrastructure and models that support robust, real-time, and personalized conversational AI capabilities.

We are seeking an experienced Staff Machine Learning Engineer to lead the development of machine learning pipelines, contribute to training and inference systems for Large Language Models (LLMs), and drive the productionization of ML models that directly impact healthcare delivery and patient outcomes. This role is both strategic and hands-on, perfect for someone excited about technical leadership in a fast-paced, mission-driven environment.

Our engineering challenges include:

• Designing scalable, high-performance infrastructure for LLM training, fine-tuning, and inference

• Building ML pipelines and tooling for experimentation, evaluation, and deployment

• Optimizing model performance and efficiency in production environments

• Collaborating cross-functionally to integrate ML solutions into end-user applications

• Maintaining compliance with healthcare standards of safety, privacy, and reliability

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field; PhD is a plus

• 7+ years of industry experience building and deploying ML systems, ideally with focus on LLMs or deep learning

• Strong software engineering skills and experience developing in Python

• Deep experience with ML frameworks like PyTorch or TensorFlow

• Familiarity with distributed training frameworks (e.g., DeepSpeed, FSDP, Horovod)

• Proven experience designing, implementing, and scaling ML pipelines for large models

• Experience with cloud platforms (e.g., AWS, GCP, Azure) and container orchestration (e.g., Kubernetes, Docker)

• Exposure to healthcare, clinical, or life sciences data is a strong plus

Other Requirements

• Strong preference for individuals who can work onsite at our HQ located in Palo Alto, CA, but we will consider candidates throughout the U.S.

As a Staff ML Engineer at Hippocratic AI, you’ll have a seat at the table in technical decision-making, partner closely with product and research leads, and help define the roadmap for scalable, safe ML systems. This role offers the chance to be deeply embedded in a team of engineers and scientists pioneering the future of healthcare-focused AI.

References

1. Polaris: A Safety-focused LLM Constellation Architecture for Healthcare,

2. Polaris 2:

3. Personalized Interactions:

4. Human Touch in AI:

5. Empathetic Intelligence:

6. Polaris 1:

7. Research and clinical blogs:

***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.