We are seeking a highly skilled and experienced Machine Learning Engineer. You’ll be joining a team developing a unique, neurosymbolic AI architecture, combining transformers, GNNs, planning / control algorithms (e.g. MCTS) and other neural and symbolic architectures. The problem we are solving is creating reliable, generalizable and knowledge grounded AI for use cases across healthcare, with a virtuous cycle of knowledge diffusion and feedback. Our early adopters are providers to enhance decision making, pharma for enhanced real-word evidence, and insurers for enhanced predictions.
Key Responsibilities:
- Working as part of our team researching and engineering ML systems that can reason about medical knowledge.
- Training large neural networks and machine learning systems at scale in HPC clusters.
- Architecting and implementing ML training, validation, and inference pipelines from idea to product.
- Researching and implementing state of the art algorithms from literature.
- Using good software engineering practices to write production-grade software.
- Innovating and defining novel creative solutions to deep problems using first-principles thinking, and communicating your ideas to the team.
Key Requirements:
- Strong ML background with exposure to transformers based architectures. RL, graphs/GNNs and MuZero style patterns are a plus.
- Proven track record in writing scalable, performant and clean python code for production and developing effective ML pipelines from initial idea to deployable product.
Why Work with Us
● Office based in San Francisco
● Competitive salary and stock
● Opportunity to publish research and co-author patents
Job Type: Full-time
Compensation package:
Experience level:
Application Question(s):
- Do you have a ML background with exposure to transformers based architectures, Reinforcement Learning, graphs/GNNs and MuZero style patterns?
Experience:
- Python: 3 years (Preferred)
- SQL: 3 years (Preferred)
Ability to Commute:
- San Francisco, CA 94103 (Required)
Work Location: In person