ML Engineer II, World Models
Job Description:
• Develop multimodal world-model architectures that ingest and fuse camera, LiDAR/depth, and robot state and produce short-horizon predictions.
• Build and maintain training pipelines: dataset construction, tokenization/backbones, distributed training, and ablation frameworks.
• Define model evaluation metrics and regression suites that reflect real robot outcomes.
• Create visualization/debug tooling for temporal predictions (rollouts, replays, overlays, failure case inspection).
• Optimize and distill models for edge deployment; benchmark latency, memory, and stability on target hardware.
• Collaborate with the AI Platform team to integrate the world model into autonomy stacks and validate behavior.
• Work with Operations to identify failure modes in the field and drive data curation and model iteration.
Requirements:
• Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
• 3+ years of experience building and training deep learning models in robotics, autonomy, or perception.
• Strong proficiency with PyTorch and modern training workflows (distributed training, mixed precision, profiling).
• Experience working with multimodal sensor data (cameras + LiDAR/depth) and temporal models.
Benefits:
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• Develop multimodal world-model architectures that ingest and fuse camera, LiDAR/depth, and robot state and produce short-horizon predictions.
• Build and maintain training pipelines: dataset construction, tokenization/backbones, distributed training, and ablation frameworks.
• Define model evaluation metrics and regression suites that reflect real robot outcomes.
• Create visualization/debug tooling for temporal predictions (rollouts, replays, overlays, failure case inspection).
• Optimize and distill models for edge deployment; benchmark latency, memory, and stability on target hardware.
• Collaborate with the AI Platform team to integrate the world model into autonomy stacks and validate behavior.
• Work with Operations to identify failure modes in the field and drive data curation and model iteration.
Requirements:
• Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
• 3+ years of experience building and training deep learning models in robotics, autonomy, or perception.
• Strong proficiency with PyTorch and modern training workflows (distributed training, mixed precision, profiling).
• Experience working with multimodal sensor data (cameras + LiDAR/depth) and temporal models.
Benefits:
Apply Now
Apply Now