Scale AI
Full TimeYou'll support day-to-day robotics lab operations in San Francisco, maintaining equipment, managing inventory, and providing technical support for data collection. The role requires five-plus years in hardware labs or manufacturing, with hands-on robotics, AI, or computer vision experience and strong project management skills. You'll partner across teams to bring up new robots, track system performance, and develop expertise in imaging, sensor fusion, and autonomy concepts. Based onsite in San Francisco. Salary $134,400–$168,000.
Written from this posting by Neural Jobs AI. The full description is below.
Scale's Robotics business is rapidly expanding, supporting customers building physical AI. This position will be a key contributor in supporting data collection production in our San Francisco Robotics Lab and prototyping new embodiments. This is a first-of-its-kind role at Scale, and you will have the opportunity to advance Scale's robotics offerings.
This is an onsite, hands-on lab role based in San Francisco.
Lab Operations:
Technical Ownership:
Cross-Functional Collaboration:
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About Us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
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Scale AI provides the data infrastructure behind modern AI: high-quality training data, human and model-based evaluation, and tooling for deploying and testing frontier models.
Founded in 2016, Scale works with AI labs, enterprises and government to turn raw data into the labelled, evaluated datasets that machine learning systems are built on.
United States
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