Research Engineer - AI/RL Infrastructure

Applied Intuition•Sunnyvale, CA

4d•Onsite

About The Position

Applied Intuition is seeking a passionate Research Engineer (AI/RL Infrastructure) to join the Research Group. This role is for engineers who design, build, and operate state-of-the-art, large-scale ML systems and work closely with researchers to develop and accelerate the core platform powering next-generation physical AI systems. The Research Group focuses on creating cutting-edge technology for next-generation physical AI, particularly in end-to-end autonomous driving and robotic generalist applications. The group comprises leading experts with significant academic and industry contributions. Researchers are supported by industry-leading tools and infrastructure, enabling access to millions of miles of data and deployment of developed methods into various autonomous and robotic systems. The role involves contributing to research, learning best practices in autonomy and robotics, and operating within a fast-paced, customer-focused culture. Improvements deployed to the system immediately benefit customers and the business. The company is open to all years of experience, with a preference for Senior/Staff level experience, and considers candidates with potential Tech Lead and Manager capacity.

Requirements

Experience building and operating production-grade software systems across the full machine learning lifecycle, including training, evaluation, data, and deployment.
Opinions about building a company-wide platform for ML training, evaluation, and deployment.
Experience with performance engineering and compute acceleration for large-scale ML training, including profiling, bottleneck analysis, and optimization.
Strong systems-level debugging skills to diagnose and resolve issues in large-scale distributed training, spanning model code, data pipelines, runtimes, and cluster infrastructure.
Deep familiarity with the open-source ML and systems ecosystem, with judgment on when to adopt open source versus build in-house.
Technical experience in: Pytorch, CUDA, Ray, Flyte, K8s.

Nice To Haves

Industry experience on relevant topics (self-driving application preferred).

Responsibilities

Design and build training and evaluation infrastructure to support current AI research directions, orchestrating massive GPU clusters to process PBs of multimodal sensor data.
Build robust benchmarking, continuous evaluation, and regression tracking systems to measure model performance across diverse, long-tail real-world driving distributions.
Develop large-scale data sampling, dataset generation, and advanced data curation pipelines, leveraging state-of-the-art AI models to power a closed-loop data flywheel.
Enable high-throughput distributed training across heterogeneous cloud environments, focusing on reliability, efficiency, and cost-aware scaling.
Collaborate closely with AI research, autonomy, and platform teams to translate cutting-edge research into production-ready systems.