Engineering Manager, Robotics

Posted Yesterday
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2 Locations
212K-254K Annually
Senior level
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role
Lead the Robotics team at Scale AI, managing product delivery and development, mentoring teams, and driving innovation in robotics data solutions.
Summary Generated by Built In

Scale’s Robotics Data Engine is an industry-leading solutions for improving and deploying robotics. Leading companies work with Scale to collect robotics data, create high-quality labeled datasets, and evaluate model improvement.

 

About This Role:
As the Engineering Manager for the Robotics team at Scale AI, you will lead teams responsible for our data products spanning robotics data collection, 2D and 3D data annotation, and data management. This critical role encompasses leadership across Product Management and Engineering teams to deliver solutions that align with the strategic needs of our customers. You will also represent the company to customer and industry partners and set the pace for innovation and operational excellence in our offerings.


Responsibilities:

  • Lead roadmapping, product delivery, and development for our Robotics Data Engine product suite (Robotics, Data Labeling, Data Management)
  • Manage diverse teams of Product Managers, Engineering Managers, and Field Engineers
  • Create industry-leading solutions to deliver measurable improvements to customers in data quality, cost, and speed
  • Build and mentor a high-performing technical team
  • Maintain strong relationships with partners, key customers, and internal stakeholders

Qualifications:

  • 10+ years of professional experience and 5+ years managing teams
  • 5+ years of experience in robotics
  • Deep expertise in robotics data. You have spent time hands-on with robotics and understand the dynamics of data collection, management, and sharing
  • Demonstrated experience in managing large-scale human operations systems
  • Track record of shipping excellent products and operating effectively in dynamic environments
  • Proven leadership capabilities with a track record of managing high-performing cross-functional teams

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, determined by work location and additional factors, including job-related skills, experience, 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 reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:

$212,000$254,400 USD

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, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI.  Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. 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 Labeling
Data Management
Robotics
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The Company
San Francisco, CA
523 Employees
On-site Workplace
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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