Senior/Staff Machine Learning Engineer, Perception
On-siteSouth San Francisco, California, United States
Job Summary
Develop real-time perception models for open-world obstacle and terrain understanding, building multi-modal fusion that combines camera and LiDAR into a unified 3D/BEV representation robust to occlusions and sensor degradation. Optimize models for low-latency inference on resource-constrained hardware while designing auto-labeling pipelines that leverage foundation models to scale data collection. Curate large multi-sensor datasets, surface failures quickly with visualization tooling, and analyze performance metrics to iterate on algorithms for improved accuracy and efficiency. This role sits at the center of a shift toward learned, dense scene representations and foundation-model-driven data engines, validating work on real machines globally. Join a tight-knit team of engineers transforming heavy machinery into intelligent, autonomous systems for agriculture and turf.
Required Qualifications
- A MS/PhD in Computer Science, AI, or a related field, or 6+ years of industry experience building vision-based perception systems
- Deep expertise developing and deploying modern perception models: detection, segmentation, mono/stereo/metric depth, BEV/occupancy, sensor fusion, and 3D scene understanding
- Fluency adapting, fine-tuning, and distilling large pre-trained vision and vision-language models
- Strong grounding in multi-sensor integration (camera, LiDAR, radar): calibration, spatiotemporal sync, and cross-modal fusion
- Experience handling large datasets efficiently and organizing them for labeling, training and evaluation
- Fluency in Python with PyTorch/TensorFlow/OpenCV and the ability to write efficient, production-ready code for real-time systems
- Proven ability to design experiments, analyze metrics (mAP, IoU, latency/throughput, and calibration/ECE), and optimize to meet stringent real-world performance and safety requirements
- Experience architecting multi-sensor ML systems from scratch
- Experience building auto-labeling / data-engine flywheels at scale
- Experience with compute-constrained pipelines including optimizing models to balance the accuracy vs. performance tradeoff, leveraging TensorRT, model quantization, etc.
- Experience with compute-constrained deployment: TensorRT, model quantization, and custom CUDA operations
- Publications at top-tier perception/robotics venues (CVPR, ICRA, CoRL, RSS, etc.)
Desired Qualifications
- Familiarity with emerging predictive world models for anticipation, anomaly detection, or closed-loop simulation, and adjacent policy paradigms such as Vision-Language-Action (VLA) and World-Action (WAM) models
- Passion for how we feed, build, move, and maintain the world
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