Join Palantir's software engineering team to enable ML models in production for critical customers.
Joblaze summary
In this role, the software engineer focuses on deploying machine learning models across diverse environments, ensuring they operate effectively under various constraints. Key skills include expertise in GPU scheduling, deployment pipelines, and observability, with a strong emphasis on maintaining software quality through continuous testing and delivery. This position is ideal for experienced engineers who thrive in complex, mission-driven settings and are passionate about leveraging AI to solve critical challenges. Palantir's team is dedicated to pushing the boundaries of AI infrastructure, catering to clients with stringent data requirements.
From the original posting
A World-Changing Company
Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more.
The Role
We are a software engineering team with expertise in enabling ML models in production. We deploy AI models to run in variety of environments: air-gapped government networks, forward-deployed defense environments, edge nodes, and enterprises with strict data sovereignty requirements. Our customers rely on us for frontier AI capabilities running on hardware they control, often with constrained GPU resources and limited direct access. Rising to that challenge and meeting those expectations is what Palantir's excels at.
We treat models like any other software: continuously tested, continually delivered, packaged for reproducible deployment, and built for long-term maintainability. You will own services end-to-end, and work across the full stack, from inference engines, GPU scheduling to deployment pipelines, observability, and integration with Palantir's platform. The goal is to deliver new models and capabilities quickly and continuously.
Join us if you want to solve problems at the intersection of infrastructure and machine learning that directly enable critical customers.