Develop metrics and large-scale evaluation pipelines to assess fidelity of Zoox's GenAI-powered autonomous vehicle simulator. Requires 5+ years in quantitative evaluation or robotics, strong Python/data analysis skills, and statistics knowledge.
Develop deep learning models using imitation and reinforcement learning for autonomous driving agents and planning. Requires advanced degree plus experience with RL, transformers, and production ML pipelines.
Build and operate scalable ML training frameworks, distributed infrastructure, and model lifecycle tools to power foundational models, reinforcement learning, and other ML use cases across Zoox's autonomous driving teams. Requires 2+ years ML infrastructure experience with PyTorch, DeepSpeed, JAX, Ray and AWS.
Build and productionize agentic LLM-powered triage systems and ML/DL pipelines to automate failure analysis for autonomous robots. Requires Master's/PhD in STEM + 4+ years production ML/NLP experience with PyTorch, RAG, Databricks, and AWS.
Develop next-generation 3D occupancy and segmentation networks for autonomous vehicles by fusing Lidar, Camera, and Radar data into temporally consistent voxel representations. Requires MS/PhD + 6+ years experience in 3D CV, multi-modal fusion, and PyTorch.
Build and deploy production GenAI/LLM applications and intelligent agents to automate workflows across Procurement, Supply Chain, Legal, Finance, HR, and Marketing. Requires 5+ years engineering experience including 2+ years production LLMs, Python, LangChain/LlamaIndex, and cloud AI/vector DB tools.
Lead and grow a team developing large-scale Reinforcement Learning and ML models for onboard autonomous vehicle behavior and driving plans. Requires MS/PhD, 5+ years production ML experience (3+ in leadership), and expertise in RL/ML for planning, LLMs, or related areas.
Lead a team developing large-scale RL and ML models for autonomous vehicle behavior planning and driving decisions. Requires RL/ML expertise, production ML pipeline experience, and 3+ years in leadership.
Optimize and deploy large multi-modal foundation models (LLMs, VLMs) for real-time inference on power-constrained vehicle SoCs. Focus on quantization, custom CUDA kernels, TensorRT pipelines, resource allocation, and low-latency concurrent C++ code for autonomous systems.
Machine Learning Engineer focused on GPU-based simulation frameworks, reinforcement learning, and bridging sim-to-real gaps for autonomous vehicle safety validation. Requires MS/PhD and strong C++/Python experience.
Build and optimize large-scale simulation and ML training pipelines on Ray and Kubernetes to validate autonomous vehicle behavior. Requires 8+ years experience and strong distributed systems background.
Develop and integrate ML detection and tracking models for autonomous vehicle perception using production pipelines and vehicle sensor data. Requires Python/PyTorch experience and familiarity with modern detection/tracking architectures.
Drive ML performance optimization initiatives to make autonomous driving models faster and more efficient using distributed training, quantization, distillation, and profiling tools.
Develops and maintains behavioral models for road users (vehicles, pedestrians, cyclists) in autonomous driving perception stack. Requires MS/PhD, 7+ years experience, deep learning expertise, Python fluency, and production ML pipelines.
Designs end-to-end perception architecture for autonomous systems, bridging sensing and ML teams. Leads hardware-software co-design, AI pipeline evolution, and cross-functional teams. Requires 8+ years in autonomy, PhD/MS in CS/Robotics, expertise in CV and sensors.
Optimizes and deploys large-scale AI models (LLMs, VLMs) for real-time inference on power-constrained vehicle hardware. Requires expertise in quantization, TensorRT compilation, custom CUDA kernels, and production C++/Python for edge devices.
Develops advanced Vision-Language-Action models for robotaxi scene understanding, detecting hazards and enabling safe driving. Leads data strategies, post-training of large models, and deployment using PyTorch and production ML pipelines. Requires MS/PhD in CS and deep learning expertise.
Develops ML models using imitation and reinforcement learning for agent behavior prediction and autonomous vehicle planning. Requires PhD +1 year or MSc +5 years experience, expertise in RL, transformers, and production ML pipelines.
Design, train, and integrate ML models for semantic map element detection in autonomous vehicles. Requires 5+ years experience, MS/PhD in CS, expertise in computer vision, deep learning, and PyTorch.
Lead technical development of ML algorithms for next-generation ML Planner. Drive innovations in imitation learning, reinforcement learning, and model scaling while mentoring ML developers.
Develops cutting-edge perception models using fused sensor data for autonomous vehicles, focusing on joint detection/tracking and embeddings for downstream systems. Requires MS/PhD in CS, PyTorch experience, and production deployment skills.
Develops and optimizes ML-driven 3D sensor simulations (cameras, lidar, radar) using GenAI and graphics techniques to generate realistic synthetic data for AV testing. Requires 2+ years experience with neural rendering (NeRFs, Gaussian Splatting), PyTorch/TensorFlow, Python/C++, and 3D math.
Develops LLM-powered tools to boost developer productivity 10x, including code assistance, automated reviews, and task automation for autonomy software. Requires 7+ years experience as full-stack/backend developer fluent in Python or C++ with LLM optimization knowledge.
Develops and optimizes ML-driven 3D sensor simulations (cameras, lidar, radar) using neural rendering techniques like NeRFs and Gaussian Splatting to generate high-fidelity synthetic data for AV AI training. Requires 5+ years experience with PyTorch/TensorFlow, Python/C++, and 3D graphics expertise.
As a Software Engineer on the Behavior Capabilities team, you will develop and implement algorithmic advancements to expand the robot's driving abilities in complex scenarios, focusing on improving trip progress and vehicle uptime.
Leads development of ML algorithms for robot perception, including scene understanding, tracking, segmentation, and multi-modal foundation models using sensor data. Requires deep expertise in deep learning, computer vision, and production ML pipelines, collaborating across autonomy teams.
Leads ML performance optimization for training and inference platforms in autonomous driving, collaborating across teams to enhance efficiency using PyTorch, TensorRT, and profiling tools. Requires strong Python/C++ skills, GPU expertise, and 4+ years in large-scale ML platforms; leads engineering team.
Develop metrics and large-scale evaluation pipelines to assess fidelity of Zoox's GenAI-powered autonomous vehicle simulator. Requires 5+ years in quantitative evaluation or robotics, strong Python/data analysis skills, and statistics knowledge.
Develop deep learning models using imitation and reinforcement learning for autonomous driving agents and planning. Requires advanced degree plus experience with RL, transformers, and production ML pipelines.
Build and operate scalable ML training frameworks, distributed infrastructure, and model lifecycle tools to power foundational models, reinforcement learning, and other ML use cases across Zoox's autonomous driving teams. Requires 2+ years ML infrastructure experience with PyTorch, DeepSpeed, JAX, Ray and AWS.
Build and productionize agentic LLM-powered triage systems and ML/DL pipelines to automate failure analysis for autonomous robots. Requires Master's/PhD in STEM + 4+ years production ML/NLP experience with PyTorch, RAG, Databricks, and AWS.
Develop next-generation 3D occupancy and segmentation networks for autonomous vehicles by fusing Lidar, Camera, and Radar data into temporally consistent voxel representations. Requires MS/PhD + 6+ years experience in 3D CV, multi-modal fusion, and PyTorch.
Build and deploy production GenAI/LLM applications and intelligent agents to automate workflows across Procurement, Supply Chain, Legal, Finance, HR, and Marketing. Requires 5+ years engineering experience including 2+ years production LLMs, Python, LangChain/LlamaIndex, and cloud AI/vector DB tools.
Lead and grow a team developing large-scale Reinforcement Learning and ML models for onboard autonomous vehicle behavior and driving plans. Requires MS/PhD, 5+ years production ML experience (3+ in leadership), and expertise in RL/ML for planning, LLMs, or related areas.
Lead a team developing large-scale RL and ML models for autonomous vehicle behavior planning and driving decisions. Requires RL/ML expertise, production ML pipeline experience, and 3+ years in leadership.
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Optimize and deploy large multi-modal foundation models (LLMs, VLMs) for real-time inference on power-constrained vehicle SoCs. Focus on quantization, custom CUDA kernels, TensorRT pipelines, resource allocation, and low-latency concurrent C++ code for autonomous systems.
Machine Learning Engineer focused on GPU-based simulation frameworks, reinforcement learning, and bridging sim-to-real gaps for autonomous vehicle safety validation. Requires MS/PhD and strong C++/Python experience.
Build and optimize large-scale simulation and ML training pipelines on Ray and Kubernetes to validate autonomous vehicle behavior. Requires 8+ years experience and strong distributed systems background.
Develop and integrate ML detection and tracking models for autonomous vehicle perception using production pipelines and vehicle sensor data. Requires Python/PyTorch experience and familiarity with modern detection/tracking architectures.
Drive ML performance optimization initiatives to make autonomous driving models faster and more efficient using distributed training, quantization, distillation, and profiling tools.
Develops and maintains behavioral models for road users (vehicles, pedestrians, cyclists) in autonomous driving perception stack. Requires MS/PhD, 7+ years experience, deep learning expertise, Python fluency, and production ML pipelines.
Designs end-to-end perception architecture for autonomous systems, bridging sensing and ML teams. Leads hardware-software co-design, AI pipeline evolution, and cross-functional teams. Requires 8+ years in autonomy, PhD/MS in CS/Robotics, expertise in CV and sensors.
Optimizes and deploys large-scale AI models (LLMs, VLMs) for real-time inference on power-constrained vehicle hardware. Requires expertise in quantization, TensorRT compilation, custom CUDA kernels, and production C++/Python for edge devices.
Develops advanced Vision-Language-Action models for robotaxi scene understanding, detecting hazards and enabling safe driving. Leads data strategies, post-training of large models, and deployment using PyTorch and production ML pipelines. Requires MS/PhD in CS and deep learning expertise.
Develops ML models using imitation and reinforcement learning for agent behavior prediction and autonomous vehicle planning. Requires PhD +1 year or MSc +5 years experience, expertise in RL, transformers, and production ML pipelines.
Design, train, and integrate ML models for semantic map element detection in autonomous vehicles. Requires 5+ years experience, MS/PhD in CS, expertise in computer vision, deep learning, and PyTorch.
Lead technical development of ML algorithms for next-generation ML Planner. Drive innovations in imitation learning, reinforcement learning, and model scaling while mentoring ML developers.
Develops cutting-edge perception models using fused sensor data for autonomous vehicles, focusing on joint detection/tracking and embeddings for downstream systems. Requires MS/PhD in CS, PyTorch experience, and production deployment skills.
Develops and optimizes ML-driven 3D sensor simulations (cameras, lidar, radar) using GenAI and graphics techniques to generate realistic synthetic data for AV testing. Requires 2+ years experience with neural rendering (NeRFs, Gaussian Splatting), PyTorch/TensorFlow, Python/C++, and 3D math.
Develops LLM-powered tools to boost developer productivity 10x, including code assistance, automated reviews, and task automation for autonomy software. Requires 7+ years experience as full-stack/backend developer fluent in Python or C++ with LLM optimization knowledge.
Develops and optimizes ML-driven 3D sensor simulations (cameras, lidar, radar) using neural rendering techniques like NeRFs and Gaussian Splatting to generate high-fidelity synthetic data for AV AI training. Requires 5+ years experience with PyTorch/TensorFlow, Python/C++, and 3D graphics expertise.
As a Software Engineer on the Behavior Capabilities team, you will develop and implement algorithmic advancements to expand the robot's driving abilities in complex scenarios, focusing on improving trip progress and vehicle uptime.
Leads development of ML algorithms for robot perception, including scene understanding, tracking, segmentation, and multi-modal foundation models using sensor data. Requires deep expertise in deep learning, computer vision, and production ML pipelines, collaborating across autonomy teams.
Leads ML performance optimization for training and inference platforms in autonomous driving, collaborating across teams to enhance efficiency using PyTorch, TensorRT, and profiling tools. Requires strong Python/C++ skills, GPU expertise, and 4+ years in large-scale ML platforms; leads engineering team.