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.