Applied MLE Evaluations

Hippocratic AI has developed a safety-focused Large Language Model (LLM) for healthcare. The company believes that a safe LLM can dramatically improve healthcare accessibility and health outcomes in the world by bringing deep healthcare expertise to every human. No other technology has the potential to have this level of global impact on health.

• Innovative Mission: We are developing a safe, healthcare-focused large language model (LLM) designed to revolutionize health outcomes on a global scale.

• Visionary Leadership: Hippocratic AI was co-founded by CEO Munjal Shah, alongside a group of physicians, hospital administrators, healthcare professionals, and artificial intelligence researchers from leading institutions, including El Camino Health, Johns Hopkins, Stanford, Microsoft, Google, 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.

• World-Class Team: Our team is composed of leading experts in healthcare and artificial intelligence, ensuring our technology is safe, effective, and capable of delivering meaningful improvements to healthcare delivery and outcomes.

For more information, visit www.HippocraticAI.com.

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.

As an Applied Machine Learning Engineer – Evaluations at Hippocratic AI, you’ll be at the core of how we measure, understand, and improve our voice-based generative AI healthcare agents.

Your work will translate complex, qualitative notions of empathy, safety, and accuracy into quantitative evaluation signals that guide model iteration and deployment.

You’ll design and implement evaluation harnesses, analysis tools, and visualization systems for multimodal agents that use language, reasoning, and speech.

Partnering closely with research, product, and clinical teams, you’ll ensure every model update is grounded in data, validated against real-world scenarios, and continuously improving in both intelligence and bedside manner.

This is a hands-on, experimental role for ML engineers who care deeply about quality, safety, and user experience—and who thrive at the intersection of research and product.

• Design and implement evaluation harnesses for multimodal agent tasks, spanning speech, text, reasoning, and interaction flows.

• Build interactive visualization and analysis tools that help engineers, researchers, and clinicians inspect model and UX performance.

• Define, automate, and maintain continuous evaluation pipelines, ensuring regressions are caught early and model releases improve real-world quality.

• Collaborate with product and clinical teams to translate qualitative healthcare goals (e.g., empathy, clarity, compliance) into measurable metrics.

• Analyze evaluation data to uncover trends, propose improvements, and support iterative model tuning and fine-tuning.

Must Have:

• 4+ years of experience in applied ML, ML engineering, or AI evaluation, with a focus on building and analyzing model pipelines.

• Strong skills in Python, with experience in data processing, experiment tracking, and model analysis frameworks (e.g., Weights & Biases, MLflow, Pandas).

• Familiarity with LLM evaluation methods, speech-to-text/text-to-speech models, or multimodal systems.

• Understanding of prompt engineering, model fine-tuning, and retrieval-augmented generation (RAG) techniques.

• Comfortable collaborating with cross-functional partners across research, product, and design teams.

• Deep interest in AI safety, healthcare reliability, and creating measurable systems for model quality.

• Experience building human-in-the-loop evaluation systems or UX research tooling.

• Knowledge of visualization frameworks (e.g., Streamlit, Dash, React) for experiment inspection.

• Familiarity with speech or multimodal model evaluation, including latency, comprehension, and conversational flow metrics.

If you’re passionate about understanding how AI behaves, measuring it rigorously, and helping shape the next generation of clinically safe, empathetic voice agents, we’d love to hear from you.

Join Hippocratic AI and help set the benchmark for evaluation-driven AI development in healthcare.

***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