Machine Learning Engineer responsible for implementing and maintaining data science models in bpx’s machine learning studio. Bridging data science and computational needs to achieve business outcomes.
About the role
- Machine Learning Engineer developing advanced SLAM systems for autonomous trucking environments at Bot Auto. Collaborating with cross-functional teams to optimize mapping solutions and ensure operational stability.
Responsibilities
- Be part of a multidisciplinary team of research scientists and engineers using an AI-first approach to enable safe self-driving at scale.
- Design and develop advanced SLAM systems to create, automate, and optimize large-scale 3D map-building pipelines for autonomous trucking environments.
- Implement robust, precise, and real-time onboard state estimation algorithms capable of maintaining accuracy and robustness under challenging conditions.
- Build and refine deep-learning-based observation models that enable resilient perception and localization performance across diverse and complex real-world scenarios.
- Integrate and validate localization and mapping solutions across onboard and offline systems, ensuring scalability, efficiency, and long-term operational stability.
- Collaborate cross-functionally with perception, planning, control, and systems teams to align mapping and localization outputs with broader autonomy goals.
Requirements
- Master’s or PhD in Robotics, Computer Science, Electrical Engineering, or a related field.
- Strong background in robotics, simultaneous localization and mapping (SLAM), or 3D vision theory and practice.
- In-depth knowledge and extensive experience in deep learning, computer vision, and modern transformer architectures.
- Demonstrated experience implementing SLAM/localization/3D vision systems in camera-based or LiDAR-based domains (or both) in real-world environments.
- Solid software engineering skills in C++ and Python.
- Experience with sensor fusion involving LiDAR, cameras, IMU, and GNSS/RTK.
- Strong quantitative foundation in linear algebra, probability, statistics, estimation theory, and optimization.
- Comfortable working in a fast-paced, multi-disciplinary autonomy environment with a hands-on, problem-solving mindset.
- Excellent communication skills and the ability to collaborate effectively across teams.
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