layiq
/la·yiq/adjective
worthy; deserving; fitting; suitable.
A role, opportunity, or path that merits attention, time, and pursuit.
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Job opportunity
ML & Cloud Infrastructure Engineer
Gritt Robotics Inc
South San Francisco, California, United States
Source: Gritt Robotics Inc careers · View original posting
Employer job description
From Gritt Robotics Inc's posting. “We” and “our” refer to the employer.
Gritt 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 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 & 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.