ML & Cloud Infrastructure Engineer

Gritt https://gritt.ai/ is developing physical AI to automate the construction of large-scale infrastructure around the globe. Gritt’s systems are already deployed commercially in difficult outdoor environments, and are helping to build critical energy infrastructure. The founding team https://www.gritt.ai/team comprises experts in robotics and AI from Carnegie Mellon, Stanford and MIT. Gritt is a Series A company backed by marquee VCs. Role: Software - ML & Cloud Infrastructure Location: SF Bay Area (in-person) About the role We’re looking for an experienced ML & Cloud Infrastructure Engineer to join our team. As an early member, you will play a pivotal role in architecting scalable cloud infrastructure for our AI and data pipelines. You'll need to thrive in a fast-paced startup environment where you'll wear multiple hats and have a direct impact on our product's evolution. Ideally, you have a proven track record of developing and deploying high-performance ML and cloud pipelines in production, and you're passionate about pushing the boundaries of what's possible in robotics with AI. What you’ll get to work on - Develop and deploy scalable AI training and validation pipelines in the cloud. - Spin up distributed pipelines for data ingestion, pre-processing, training and evaluation. - Deploy monitoring and CI/CD pipelines. - Enable large-scale evaluation of AI models via cloud-based metrics. - Enable large-scale evaluation of autonomy software and models via simulations in the cloud. - Optimize performance, I/O and GPU utilization. - Build tooling and dashboards for rapid experimentation, orchestration and visualization. - Work with other teams to integrate cloud tooling into workflows. What we look for - Degree in computer science or related engineering disciplines (or equivalent experience). - 4+ years of experience deploying high-performance ML pipelines in production. - Proficient in Python and comfortable with C++/Go. - Experience with ML frameworks like PyTorch. - Experience with IO and data-loading workflows, including formats like Parquet, HDF5, TFRecord etc. - Experience with deploying on cloud platforms like AWS, GCP or Azure. - Experience with tooling like Docker, Kubernetes, and Airflow. - Should be comfortable taking ownership of tasks with light supervision. - Must have excellent problem-solving skills. - Legally authorized to work in the United States.