Senior Machine Learning Engineer

Salary Range: 140000 to 175000 (Currency: USD) (Pay period: per-year-salary)

Responsibilities:

• Architect Multi-Modal Vision Systems: You will design and train models that fuse 2D inputs with 3D geometry to solve complex grasping and scene understanding tasks.
• Lead End-to-End Model Deployment: You will own the transition from research to reality. This includes model graph optimization, quantization (TensorRT), and runtime integration to ensure low-latency inference on our edge compute hardware (NVIDIA Orin).
• Drive Technical Excellence: As a senior member of the team, you will conduct rigorous code reviews, mentor junior engineers, and contribute to the strategic perception roadmap.
• Own the Data Strategy: You will take ownership of our existing labeled dataset and pipeline. You will identify bottlenecks, improve data quality, implement active learning strategies to systematically resolve edge cases and improve model robustness.
• Ensure Production Reliability: You will write high-performance production code (Python/C++) to seamlessly integrate perception outputs into the broader robotic control stack, prioritizing safety and system stability.

Skills:

• 5+ Years of Experience in Computer Vision and Machine Learning, with a track record of shipping ML products to the physical world (Robotics, AV, or IoT).
• Expert-level Python and PyTorch skills. Working knowledge of C++ for deployment and system integration.
• Experience with 2D Vision (YOLO, MaskRCNN, Transformers) and 3D Vision (PointNet, grasp generation, multi-view geometry, camera calibration).
• You are proficient with inference optimization tools such as TensorRT, ONNX Runtime, or CUDA to maximize hardware utilization.
• You have experience curating large-scale datasets, detecting statistical bias, and automating quality assurance within the ML pipeline.
• You can translate high-level product requirements into specific engineering tasks and explain technical trade-offs to non-expert stakeholders.
• Familiarity with Docker, AWS/GCP (S3, EC2), labeling platforms and experiment tracking tools.