Top Tech Jobs & Startup Jobs

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 and operate petabyte-scale end-to-end data pipelines for autonomous driving: onboard upload, cloud preprocessing, dataset production, and model training input. Build data cleaning, annotation QA, lineage, versioning, metadata management, and high-throughput distributed processing. Optimize I/O, memory, transmission, and containerized deployments; support cross-team collaboration and algorithm iteration.

Design and implement real-time lane-fusion algorithms that combine multi-source perception and temporal data to produce stable 3D lane and road representations. Research solutions for occlusion, fragmentation, forks/merges, and construction zones. Build a 3D laneline pipeline with camera perception, optimize for low latency and high availability on safety-critical vehicle platforms, and collaborate with cross-functional teams to deploy on target systems.

Lead the development of AI applications, ensuring high reliability and performance while collaborating with teams to identify use cases and implement scalable solutions. Drive AI methodologies, governance, and continuous improvement in the organization.

Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision). Implement robust Python production code, build export/calibration/benchmarking workflows, collaborate across research, field-testing, vehicle software, and training infrastructure teams, and curate evaluation datasets and metrics to validate model accuracy, latency, and numerical consistency for on-vehicle inference.

Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision, INT8/FP4), build robust export/calibration/benchmarking/validation tooling, collaborate with research, field-testing, simulation and vehicle software teams, curate evaluation datasets and metrics, analyze numerical errors and performance trade-offs, and develop orchestration workflows for on-vehicle inference and model sign-off.

Develop and deploy 2D traffic sign detection models for autonomous driving. Responsibilities include scenario analysis, dataset curation, model training and evaluation, optimization and quantization, building evaluation pipelines, long-tail data mining, and validating performance on edge/onboard platforms while collaborating across data, infrastructure, and deployment teams.

Develop and optimize end-to-end data pipelines for autonomous driving, ensure data quality, and support model training through efficient data management processes.