Job description

Gritt gritt.ai is an intelligent system that combines robotics and AI to build the infrastructure that pulls society forward. Gritt deploys via simple attachments to common equipment found on construction sites and autonomously performs labor-intensive tasks, verification, and planning. Gritt systems are already building critical infrastructure in the harshest outdoor environments, starting with large-scale solar. The founding team gritt.ai includes experts in robotics and AI from Carnegie Mellon, Stanford, and MIT. Gritt is backed by Obvious Ventures, Union Square Ventures, First Round Capital, Climactic, Congruent Ventures, and other leading firms. Role: Software - ML Infrastructure Engineer Location: SF Bay Area (in-person) About the role We’re looking for an experienced ML 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.

Requirements

Must-haves

Role: Software - ML Infrastructure Engineer
Location: SF Bay Area (in-person)
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.

Nice-to-haves

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Keywords to mirror

aimlci/cdpythongoawsgcpazuredockerkubernetes

Platform: ashby
Effort: quick

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