Senior Machine Learning Engineer – Perception & Embodied AI

Job Description

Responsibilities ​

• End-to-End Model Lifecycle: Own the design, training, validation, and deployment of deep learning models for core perception tasks such as:

• 3D Object Detection and Tracking (vehicles, pedestrians, cyclists).

• Real-time map detection of the drivable world (lanes, road boundaries, traffic signs).

• Multi-Modal Sensor Fusion (Camera, LiDAR, Radar).

• Production Pipeline: Build and scale the ML training infrastructure, including data mining and loading, multi-stage training and evaluation, to streamline model development.

• Performance Optimization: Improve model performance through data iterations, parameter tunings, training strategy and architecture updates to produce reliable models that meet and the strict real-time, low-latency requirements on the vehicle's embedded hardware.

• Model Debugging: Conduct rigorous, data-driven analysis to identify, debug, and resolve performance degradations and failures, specifically targeting long-tail and adversarial scenarios (e.g., adverse weather, sensor noise, occlusions).

• Metric Definition: Define and implement robust model-level metrics to aid model development.

• System Integration: Work closely with the Safety, Systems, and other engineering functions to integrate Perception outputs.

Skills & Experience ​

• BS, MS or PhD in Computer Science, Machine Learning, Robotics, or a related quantitative field.

• 5+ years of professional experience with a focus on Computer Vision, Deep Learning, and Perception in a production environment.

• Deep hands-on experience with modern deep learning frameworks (e.g., PyTorch or TensorFlow) for training, experimentation, and debugging complex DNNs.

• Proven experience working with and fusing data from multiple sensor modalities (Camera, LiDAR, and/or Radar).

• Practical experience deploying and optimizing ML models for resource-constrained, real-time embedded systems.

• Demonstrated ability to drive model improvements through large-scale data analysis, error logging, and data curation.

Bonus: 

• Expertise with Transformer-based models for 3D detection, tracking, and scene understanding.

• Technical leadership experience, including mentoring junior engineers and leading major feature development from concept to launch.

• Contributions to relevant academic publications (CVPR, ECCV, ICCV, IROS, NeurIPS etc.).

Remote: This role is based remotely but if you live within a 50-mile radius of Atlanta, Austin, Detroit, Warren, Milford or Mountain View, you are expected to report to that location three times per week, at minimum. 

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area. 

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