About the position We're looking for a Staff Software Engineer – Computer Vision Deployment to build and scale the infrastructure that powers our AI-driven warehouse intelligence platform. You'll own the end-to-end lifecycle of computer vision models — from training pipelines through optimized cloud deployment — ensuring our cutting-edge computer vision and multi-modal AI systems run reliably and efficiently in production. Your work will directly enable the real-time perception and autonomous decision-making capabilities at the core of our platform. This is a deeply technical role at the intersection of machine learning, distributed systems, and cloud infrastructure. You'll design scalable GPU compute clusters, build robust orchestration pipelines, and optimize model serving for low-latency inference at scale. You'll work closely with our research scientists, computer vision engineers, and product teams to bridge the gap between experimental models and production-ready systems that operate across diverse warehouse environments. We've found tremendous value in collaborative problem-solving, thus our team works from our SF office three days a week.

Responsibilities

• Develop and maintain distributed cloud GPU infrastructure for large-scale world model training and low-latency inference.
• Build end-to-end computer vision pipelines — from data ingestion and preprocessing through model training, evaluation, and deployment — and integrate them into core product workflows.
• Deploy and optimize state-of-the-art machine learning models in the cloud using model serving platforms and inference optimization techniques, including VLMs and VLAs.
• Design and operate orchestration systems that enable both engineers and non-engineers to build and manage data and ML pipelines.
• Establish monitoring, benchmarking, and evaluation frameworks to ensure model performance and reliability in production environments.

Requirements

• B.S. / M.S. in Computer Science, Robotics, or similar technical field, or equivalent practical experience.
• 7+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure — developing, scaling, training, deploying, and optimizing large-scale ML systems from data to model.
• Track record of deploying computer vision models in production environments with real-world constraints.
• Experience with distributed messaging and compute systems (Kafka, gRPC, ROS2, or similar).
• Strong programming skills in Python with solid software engineering practices.

Nice-to-haves

• Experience developing, running, and managing orchestration systems (Flyte, Temporal, Airflow, or similar) for ML and data pipelines.
• Proficiency with ML frameworks (PyTorch, TensorFlow, DeepSpeed) and model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton Inference Server, or similar).
• Deep understanding of state-of-the-art machine learning models such as auto-regressive transformers and familiarity with inference optimization techniques (TensorRT, quantization, custom kernels).
• Experience with C++ or CUDA programming for GPU acceleration.
• Prior experience working at autonomous vehicles or robotics companies.

Benefits

• top-tier medical, dental, and vision coverage
• 401k with employer matching
• parental leave
• unlimited vacation