Research Engineer - AI/RL Infrastructure

Responsibilities

• Design and build training and evaluation infrastructure to support AI research, orchestrating massive GPU clusters to process petabytes of multimodal sensor data
• Build robust benchmarking, continuous evaluation, and regression tracking systems to measure model performance
• Develop large-scale data sampling, dataset generation, and advanced data curation pipelines 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

Requirements

• Experience building and operating production-grade software systems across the full machine learning lifecycle (training, evaluation, data, and deployment)
• Experience with performance engineering and compute acceleration for large-scale ML training, including profiling and bottleneck analysis
• Strong systems-level debugging skills to resolve issues in large-scale distributed training across model code, data pipelines, and cluster infrastructure
• Deep familiarity with the open-source ML and systems ecosystem
• Technical proficiency in PyTorch, CUDA, Ray, Flyte, and K8s

Preferred Qualifications

• Senior or Staff level experience
• Industry experience in relevant domains, such as self-driving applications

About the Company

Applied Intuition is powering the future of physical AI. We are creating the digital infrastructure needed to bring intelligence to every moving machine on the planet, servicing the automotive, defense, trucking, construction, mining, and agriculture industries. Our solutions are trusted by eighteen of the top 20 global automakers and the United States military.