SACC Chicago 2025 - Job Application

Posted 4 Days Ago
Hiring Remotely in United States
Remote
Entry level
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role
This position is for candidates who met with our team at the Chicago SACC 2025 event, interested in AI and machine learning roles at Scale AI.
Summary Generated by Built In

This posting is for candidates who attended the Chicago SACC '25 and met with a member of our team.

It was great meeting you at the Chicago SACC 2025! Whether we chatted at our booth or during a session, we’re thrilled to connect with people who are passionate about pushing the boundaries of machine learning and AI.

At Scale AI, our mission is to develop reliable AI systems for the world’s most important decisions. Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. We’re currently growing our team across multiple roles.

If you're excited about the challenges we’re working on, drop your info here, and we’ll make sure someone from our team reaches out if there's a good fit with one of our open roles. Even if the timing isn’t quite right, we’d love to stay in touch.

We look forward to continuing the conversation! In the meantime, you can read more about our work at scale.com/public-sector.

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, Cisco, DLA Piper, 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 [email protected]. 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

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

Top Skills

AI
Data Technologies
Machine Learning
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The Company
San Francisco, CA
523 Employees
Year Founded: 2016

What We Do

Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.

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