Machine Learning & AI · AI Engineering

Deploy assisted pre-labeling vision models and zero-shot architectures that dramatically accelerate throughput for autonomous driving and robotics datasets.

• Fine-tune and deploy assisted pre-labeling vision models (SAM, YOLO, Mask R-CNN, 3D point cloud detectors)
• Optimize model inference latency on GPU clusters using NVIDIA TensorRT, ONNX, and Triton
• Evaluate model prediction confidence against human consensus to tighten the active learning loop
• Integrate model-assisted annotation APIs directly into customer MLOps infrastructure

• 3+ years building and deploying deep learning models in production (PyTorch, Docker, CUDA)
• Proven expertise in Computer Vision, 3D LiDAR/sensor fusion, or multimodal vision-language architectures
• Strong software engineering fundamentals in Python and cloud systems (AWS/GCP)
• Experience optimizing deep learning inference for real-time interactive applications
• Dedicated cloud GPU compute budget for experimental research
• Full healthcare coverage including mental wellness stipends
• Flexible working hours across any global timezone
• Generous hardware allowance (M3 Max Mac Book Pro or high-end Linux workstation)

YOUR NEXT OPPORTUNITY