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Research Engineer Ai Rl Infrastructure

Hybrid Vehicles💼 Full-time💰 $126,000–$126,000🗓 2026-07-24

Core

Design, build, and operate large-scale ML infrastructure to support AI research for autonomous driving and robotic generalist systems.

Role type

Senior/Staff Research Engineer (AI/RL Infrastructure)

Builds

Training and evaluation infrastructure, benchmarking systems, data sampling pipelines, and distributed training environments for physical AI.

Domain

Autonomous driving and robotics

Deliverable

production ML models | infrastructure

Required skills

Large-scale distributed training, performance engineering, compute acceleration, systems-level debugging, open-source ML ecosystem judgment, data pipeline orchestration

Preferred skills

Self-driving application experience

Technologies

Pytorch, CUDA, Ray, Flyte, K8s

Responsibilities

Design and build training/evaluation infrastructure orchestrating massive GPU clusters; Build robust benchmarking and regression tracking systems; Develop large-scale data sampling and dataset generation pipelines; Enable high-throughput distributed training across heterogeneous cloud environments; Collaborate with research teams to translate research into production-ready systems

Seniority

Senior/Staff, hands-on IC with potential Tech Lead capacity

Rewrite
## About the role and team We are looking for a passionate Research Engineer (AI/RL Infrastructure) to join the Research Group at Applied Intuition. This role is ideal for engineers who design, build, and operate state-of-the-art, large-scale ML systems and enjoy working closely with researchers to develop and accelerate the core platform powering next-generation physical AI systems. The mission of the Research Group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity; Senior/Staff level experience is strongly preferred for this role. At Applied Intuition, you will: - Design and build training and evaluation infrastructure to support our 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 We're looking for someone who has: - 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 have: - Industry experience on relevant topics (self-driving application preferred) ## What we offer Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the location listed is: $126,000 - $423,000 USD annually.
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