Gather AI builds an AI-powered drone and camera platform that autonomously monitors warehouse inventory and operations.
<p><strong>About Us</strong></p> <p>Are you ready to build the future of the supply chain? At Gather AI, we’re not just creating software; we’re pioneering a new era of warehouse intelligence. We’ve developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining “on-time, in full” delivery.</p> <p>If you’re looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We’re leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.</p> <p><strong>About the Team</strong></p> <p>This role work closely with a Senior Machine Learning Scientist and our Lead Machine Learning Infrastructure Engineer, alongside cross-functional partners in Cloud Engineering, Product, and Operations. The role spans both deep technical work and collaboration with teams closest to the customer. As a Principal-level IC, you'll have real input into technical direction — not just executing on decisions, but helping make them. </p> <p><strong>About the Role</strong></p> <p>We are looking for a <strong>Principal Machine Learning Scientist</strong> to advance the state of our computer vision systems for warehouse inventory scanning. You will work across the full ML lifecycle — from research and model architecture through training, deployment, and production monitoring — with a focus on delivering measurable improvements to detection, segmentation, and OCR accuracy across our drone and MHE Vision products.</p> <p>This role is ideal for a senior ML practitioner who wants principal-level impact on a core product, a variety of deployment targets (cloud, on-prem, embedded), and real-world data with tangible business outcomes.</p> <p><strong>What You’ll Do</strong></p> <ul> <li>Advance core computer vision model performance (object detection, segmentation, OCR) for warehouse inventory scanning across drone and MHE Vision platforms</li> <li>Own the full ML lifecycle from research and experiment design through production deployment and monitoring — applying rigorous ablation studies and SOTA methodology</li> <li>Collaborate with the ML infrastructure team on model optimization and deployment across cloud and edge inference targets (ONNX, TensorRT, quantization)</li> <li>Work with Operations and Product to understand customer needs and translate them into ML improvements with measurable business impact</li> <li>Provide technical leadership and mentorship to the ML team, raising standards for experiment design, model evaluation, and production readiness</li> <li>Explore next-generation perception capabilities, including embedded and on-prem inference optimization for new deployment targets</li> </ul> <p><strong>What You’ll Need</strong></p> <ul> <li>10+ years of experience in machine learning or computer vision</li> <li>Deep expertise in CNNs, object detection, image segmentation, and OCR using PyTorch (preferred) or TensorFlow</li> <li>Strong Python proficiency and software engineering fundamentals; hands-on experience with OpenCV and GPU computing</li> <li>Track record of delivering production ML systems at scale, including model training, evaluation, and deployment</li> <li>MS or PhD in Computer Science, Machine Learning, Robotics, or a related field</li> </ul> <p><strong>Nice to Have</strong></p> <ul> <li>Experience with drone, robotics, or autonomous systems perception</li> <li>Publications in top vision or robotics conferences (CVPR, ICCV, ICRA, NeurIPS, CoRL)</li> <li>Experience designing and deploying models for real-time inference on constrained compute platforms</li> <li>Warehouse, logistics, or supply chain domain experience</li> </ul>