Diligent Robotics

Senior/Staff ML Engineer, Robotics

Diligent Robotics

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

Senior

Tech Stack

PythonPyTorchTensorflow

About the role

  • Develop and deploy ML models for perception/navigation tasks such as object detection, semantic segmentation, tracking, scene understanding, localization, and path prediction.
  • Design and implement sensor fusion and mapping pipelines combining vision, depth, LIDAR, IMU, and other signals for robust perception and navigation in dynamic spaces.
  • Build real-time ML inference pipelines optimized for robotic hardware and embedded compute.
  • Setup data collection, labeling strategies, dataset curation, and synthetic data augmentation for training and evaluation.
  • Establish metrics, benchmarks, and test frameworks to validate ML models in both simulation and real-world environments.
  • Collaborate with robotics software engineers to integrate perception and navigation intelligence into autonomy stacks.
  • Work with operations to analyze field data, diagnose performance gaps, and iterate on model improvements.
  • Contribute to long-term ML and perception and navigation architecture decisions, influencing the roadmap for future robots.
  • Mentor junior ML engineers and contribute to building strong applied ML best practices within the team.

Requirements

  • Master’s or PhD in Computer Science, Robotics, Machine Learning, or related field.
  • 5+ years of experience in applied machine learning, computer vision, or robotics perception.
  • Strong background in deep learning frameworks (PyTorch, TensorFlow, JAX).
  • Hands-on experience with real-time perception/navigation tasks (detection, tracking, segmentation, path planning).
  • Expertise in one or more sensor modalities: RGB/depth cameras, LIDAR, radar, or multimodal fusion.
  • Experience deploying ML models on edge/embedded hardware (e.g., Jetson, TPU, ARM-based platforms).
  • Familiarity with SLAM, mapping, and navigation pipelines.
  • Solid software engineering skills in Python and C++ for ML system integration.
  • Proven ability to take ML models from research prototype to production deployment.
  • Strong debugging skills for diagnosing ML performance gaps in fielded systems.