Diligent Robotics - ML Engineer II, World Models
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. • Experience with predictive perception / world models / video prediction. • Experience deploying ML to edge devices (TensorRT/ONNX, quantization/INT8, runtime profiling). • Familiarity with ROS pipelines, sensor calibration, and autonomy stack integration. • Experience with simulation-based evaluation (Isaac Sim/Mujoco or similar) and offline replay testing.
Responsibilities
• 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.
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