Staff SLAM/ML Engineer, Spot Autonomy
Indexed description
You Will Get To
- Develop Spot’s next generation localization, mapping, perception and autonomy capabilities and deliver them to the product.
- Apply and develop novel ML-based approaches to solve complex challenges in semantically-aware navigation and localization.
- Shape the team's strategy for ML model architectures, datasets, and pipelines to achieve maximum model performance.
- Produce high-quality, performant code in C++ and Python.
- Design and execute rigorous experiments using both simulated and real-world robot data to ensure solutions not only achieve state-of-the-art performance but are also robust and computationally efficient in real-world deployments.
To succeed in this role, you should have the following skills and experience
Required
- A Masters degree in Computer Science, Robotics or related field and 3+ years of professional experience
- A solid understanding of traditional computer vision, robotics and navigation methods (SLAM) and their typical strengths & shortcomings
- First-hand experience in Machine Learning and data driven approaches to visual perception problems. A good understanding of recent ML approaches such as LLMs & ViTs.
- Experience with Machine Learning frameworks (e.g. PyTorch)
- Experienced in writing performant, well-structured, and testable C++ and Python code
- Be a team player and good communicator, able to work well in a dynamic and collaborative environment
- Have a passion for quality and autonomous robots
- A PhD in Computer Science, Robotics or related field.
- A strong background in SLAM / factor graphs (e.g. gtsam, g2o, Ceres), and ideally ML-enhanced navigation, such as semantic SLAM.
- Experience with end-2-end semantic navigation approaches.
- A track record of relevant publications or product deliverables.
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