Healthcare AI Solutions Architect

3 weeks ago


San Francisco, California, United States Ambience Healthcare Full time
About Us
Ambience Healthcare is at the forefront of developing capable AI systems for healthcare and medicine. With a growing shortage of physicians and increasing healthcare costs, the need for AI is critical. Our frontline healthcare workers are overwhelmed, spending only 27% of their day on direct patient care. We believe that people, augmented by AI, will enable us to deliver higher quality healthcare at a lower cost, improving the experience for everyone involved.

Our Vision
We aim to equip every healthcare worker with an advanced AI co-pilot. Our team collaborates with leading AI experts, including Jeff Dean, Richard Socher, Pieter Abbeel, and AIX Ventures. Headquartered in San Francisco, we've secured $100M in funding from top investors, such as Kleiner Perkins, OpenAI Startup Fund, Andreessen Horowitz, Optum Ventures, Human Capital, and Martin Ventures.

The Role
As a Staff Machine Learning Engineer, you'll own significant swaths of our AI tech stack, ensuring high standards of experimental rigor and driving decisions that prioritize impactful investments. You'll work directly with our clinical team to prototype and deploy cutting-edge machine learning systems, leading the way toward building an AI doctor. Within your first six months, you'll lay the groundwork for long-term success, establishing AI strategy, developing tooling, and training the next generation of Ambience's foundation models tailored to medicine.

What You'll Do:
  • Co-design our AI strategy, identifying the most promising large language model techniques and determining the most impactful areas to invest in across annotation, evaluation, training, monitoring, and feedback loop pipelines.
  • Advance our medical foundation models, pushing the boundaries of what's possible in healthcare AI through prototype new model architectures, training regimes, and post-processing techniques.
  • Develop rigorous evaluation pipelines, collaborating with Clinical and Engineering teams to design state-of-the-art offline and online model evaluation infrastructure.


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