Lead Machine Learning Engineer - Localization
New
M
May MobilityAutonomous Vehicles
Remote, USAFull-TimeLead
Salary235,000 - 285,000 USD per year
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Job Details
- Experience
- 7+ years of industry experience
- Required Skills
- PythonMachine LearningPyTorchC++TensorflowDeep LearningComputer Vision
Requirements
- Ph.D. or Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related field.
- 7+ years of industry experience developing and deploying ML/DL models for computer vision or localization at scale.
- Deep expertise in computer vision foundations, including object detection, segmentation, tracking, and 3D reconstruction.
- Knowledge of vectorized landmark detection, BEV-based scene representation, and temporal modeling.
- Experience with self-supervised or semi-supervised learning and vision/fusion foundation models.
- Experience with feature extraction or fusion from imagery, LiDAR, and/or radar sensors.
- Expertise in ML/DL development using PyTorch or TensorFlow.
- Strong programming skills in Python and/or C++ with modular software design experience.
- Experience with ML optimization for real-time products, such as quantization and model pruning.
- Proven leadership experience in technical roadmapping and mentoring.
Responsibilities
- Architect and drive the technical roadmap for a production-grade localization machine learning stack.
- Lead the research, design, training, and validation of advanced neural architectures, including object detection, segmentation, and 3D reconstruction.
- Drive major feature development from inception to deployment, including architecture design and code reviews.
- Own the end-to-end data strategy, defining data curation, auto-labeling, and active learning pipelines.
- Develop robust metrics and evaluation frameworks for localization performance and system reliability.
- Define failure mode and degradation criteria to ensure safety case coverage.
- Evaluate and integrate frontier techniques, such as multimodal localization and fusion foundation models.
- Drive cross-functional alignment by translating autonomy goals into technical software requirements.
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