Research Engineer - AI/RL Infrastructure
Applied Intuition is seeking a Research Engineer specializing in AI and Reinforcement Learning (RL) Infrastructure to join their Research Group. This team focuses on developing cutting-edge technology for next-generation physical AI, particularly in end-to-end autonomous driving and robotic generalist applications. The company, headquartered in Sunnyvale, California, provides digital infrastructure to bring intelligence to various moving machines across industries such as automotive, defense, trucking, construction, mining, and agriculture.
In this role, the engineer will design and build training and evaluation infrastructure to support current AI research directions, orchestrating massive GPU clusters to process petabytes of multimodal sensor data. Responsibilities include developing robust benchmarking, continuous evaluation, and regression tracking systems to measure model performance across diverse, real-world driving scenarios. The engineer will also create 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. Additionally, the role involves enabling high-throughput distributed training across heterogeneous cloud environments, focusing on reliability, efficiency, and cost-aware scaling, and collaborating closely with AI research, autonomy, and platform teams to translate cutting-edge research into production-ready systems.
Candidates should have experience building and operating production-grade software systems across the full machine learning lifecycle, including training, evaluation, data, and deployment. They should possess strong 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, is essential. Strong systems-level debugging skills to diagnose and resolve issues in large-scale distributed training, spanning model code, data pipelines, runtimes, and cluster infrastructure, are required. Deep familiarity with the open-source ML and systems ecosystem, with judgment on when to adopt open source versus build in-house, is also necessary. Technical experience in PyTorch, CUDA, Ray, Flyte, and Kubernetes is expected.
The position offers a base salary range of $126,000 to $423,000 annually, depending on experience and qualifications. Compensation includes equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life, and disability insurance coverage, 401(k) retirement benefits with employer match, learning and wellness stipends, and paid time off. Benefits are subject to change and may vary based on the jurisdiction of employment.
Applied Intuition fosters a fast-paced and customer-focused culture, emphasizing excellence in products and business operations. The company encourages engineers to take ownership of technical and product decisions, interact closely with users to collect feedback, and contribute to a thoughtful, dynamic team environment. This role offers the opportunity to work with leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions, including multiple Best Paper awards at premier conferences and journals.