Top Tech Jobs & Startup Jobs

Own performance optimization for foundation-model training across thousands of accelerators. Diagnose and resolve bottlenecks across data paths, kernels, parallelism, communication, observability, and long-running stability. Partner with modeling, infrastructure, simulation, data-mining, and robotics teams; establish reusable performance improvements and technical direction. Build profiling infrastructure and agentic automation to reproduce jobs, verify numerical parity, detect regressions, and automate optimization workflows.

Develop, evaluate, and deploy reinforcement learning methods for autonomous vehicle planning. Design real-time model architectures, build offline data-driven ML infrastructure, analyze fleet and field-testing data, optimize and deploy planning models, and collaborate with perception, software, and AI teams to deliver production autonomous driving systems.

Research, implement, and evaluate deep-learning methods for legged locomotion and whole-body control in humanoid robots. Develop end-to-end robot motion controllers using reinforcement and imitation learning, design and run experiments to address sim-to-real challenges, and integrate state-of-the-art research.

Intern will design and optimize software to deploy large-scale AI models on production vehicles, profile and improve inference across compute, memory, and I/O, reduce latency and power use on embedded automotive platforms, and deliver production-ready, fleet-scalable performance optimizations.

Design, build, and operate a production AI engineering platform that connects agents to engineering systems. Build reliable distributed services, automation, agent lifecycle and safe action execution, semantic data layers, and observability. Lead customer onboarding, gather requirements, debug production issues, and drive adoption while establishing evaluation and safety guardrails.

Lead cross-functional programs to build and operate an autonomous-driving training data lake: define roadmap, coordinate data/ML/infrastructure/annotation teams, set KPIs for data quality and pipeline reliability, manage dataset generation/versioning/lineage, identify and resolve technical bottlenecks, and improve engineering and operational processes to deliver high-quality training data at scale.