Research Engineer - AI/RL Infrastructure at APPLIED INTUITION INC

Sunnyvale, California, United States -

Full Time

Start Date

Immediate

Expiry Date

23 May, 26

Salary

423000.0

Posted On

22 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Pytorch, CUDA, Ray, Flyte, K8s, ML Lifecycle Management, Performance Engineering, Compute Acceleration, Distributed Training, Systems-Level Debugging, Data Curation, Benchmarking, Continuous Evaluation, GPU Cluster Orchestration, Large-Scale ML Systems Design, Data Sampling

Industry

Software Development

Description

About Applied Intuition Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co. We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. 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) 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. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws.

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Responsibilities

The engineer will design and build infrastructure for training and evaluation, orchestrating massive GPU clusters to process large volumes of multimodal sensor data. Responsibilities also include building robust benchmarking and continuous evaluation systems to measure model performance across diverse real-world distributions.