Machine Learning Systems Lead

3 weeks ago


San Francisco, California, United States Ambience Healthcare Full time
About Ambience Healthcare
Ambience Healthcare is dedicated to accelerating the path to safe and useful clinical superintelligence. As a mission-oriented organization, we're committed to delivering breakthrough product experiences that transform the healthcare landscape. We're seeking a talented Staff Machine Learning Engineer to join our team and help drive the development of our AI solutions.

Our Approach
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. To achieve this vision, we're building cutting-edge AI systems that integrate seamlessly with clinical workflows. Our team collaborates with leading AI experts, including Jeff Dean, Richard Socher, Pieter Abbeel, and AIX Ventures.

The Role
As a Staff Machine Learning Engineer, you'll be responsible for laying the groundwork for long-term success, establishing AI strategy, developing tooling, and training the next generation of Ambience's foundation models tailored to medicine. 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 ensure high standards of experimental rigor, drive decisions that prioritize impactful investments, and collaborate closely with clinicians to set technical direction and identify promising large language model techniques.

What You'll Do:
  • Develop rigorous evaluation pipelines, collaborating with Clinical and Engineering teams to design state-of-the-art offline and online model evaluation infrastructure.
  • 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.
  • 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.


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