Senior/Staff Machine Learning Engineer, Agtonomy

• 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
• An eagerness to get your hands dirty and agility in a fast-moving, collaborative, small team environment with lots of ownership

Desirable

• We're looking for a skilled ML engineer to build the perception systems that give our autonomous machines human-like awareness in rugged, unstructured environments
• You'll develop computer vision and machine learning systems that turns noisy camera and LiDAR data into robust 3D scene understanding — enabling heavy equipment to operate safely through dust, glare, occlusion, and whatever messy conditions a working site throws at it
• The field is moving past bounding-box detection and hand-tuned tracking toward learned, dense scene representations, foundation-model-driven data engines, and uncertainty-aware perception
• You'll be at the center of that shift
• This role is hands-on: you'll write production-grade software, distill and optimize models for embedded hardware, and validate your work on real machines at operating around the world
• Develop real-time perception models for open-world obstacle and terrain understanding
• Build multi-modal fusion that combines camera and LiDAR into a unified 3D/BEV representation, robust to occlusions, sensor degradation, and GNSS outages
• Optimize models for low-latency inference on resource-constrained hardware, balancing accuracy and performance
• Design auto-labeling pipelines that leverage foundation models and teacher-student distillation to scale labeling and close the loop from real-world field interventions
• Design data and evaluation pipelines that curate large multi-sensor datasets and surface failures fast, with strong visualization and debugging tooling
• Analyze performance metrics and iterate on algorithms to improve accuracy and efficiency of various perception subsystems