Staff Technical Product Manager

· Remote

Location

Remote

Type

Full Time

Job Description

Staff Technical Product Manager

Posted 8 Hours Ago
Easy Apply
Be an Early Applicant
3 Locations
In-Office
Senior level
Artificial Intelligence • Big Data • Machine Learning
The Data Platform for AI: High quality training and validation data for AI applications.
The Role
The Staff Technical Product Manager will own the product roadmap for Global Defense design AI products collaborate with engineers and understand defense workflows to drive product outcomes.
Summary Generated by Built In

Scale has been the leading AI data foundry helping fuel the most exciting advancements in AI including frontier model training enterprise adoption defense applications and more. Our mission is to develop reliable AI systems for the world’s most important decisions.
Scale’s Global Public Sector (GPS) team is growing quickly and joining it is an opportunity to work on one of the most exciting and quickly expanding teams at Scale. This team is responsible for generating executing and fostering Scale’s work with governments and government-backed entities.

This is a defense role. We’re looking for a Staff Technical Product Manager to own critical product areas within Scale’s Global Defense business serving allied defense and national security customers. You’ll be a critical builder shaping and developing Scale’s global defense products while being the primary technical expert to our global customers.

The ideal candidate brings together a strong engineering or ML background entrepreneurial drive the eagerness to be a hands-on builder and the ability to get in front of senior technical and government stakeholders to drive mission-critical outcomes.

You Will:

  • Own the product roadmap and execution for one or more product areas within Global Defense driving what gets built and how it ships
  • Be hands-on: write specs prototype solutions dig into technical architecture with engineers and make product calls grounded in real technical understanding
  • Design and ship AI-powered products and tooling for defense and national security workflows working side-by-side with engineering and ML teams
  • Use customer context to inform what you build — understand allied defense workflows deeply enough to make opinionated product decisions not just relay requirements
  • Navigate the unique constraints of defense product development across allied nations — classification environments accreditation processes air-gapped deployments — or be ready to learn these fast
  • Operate like a founding team member: wear multiple hats move fast unblock yourself and drive outcomes across engineering operations and go-to-market

Ideally You’d Have

  • Builder mentality: you’re energized by going from zero to one not by managing from a distance
  • 8+ years of experience in software engineering ML engineering or a deeply technical product role where you were hands-on with what shipped
  • Technical fluency: software engineering or ML background (master’s degree in computer science or equivalent experience). Experience training or evaluating models is a plus
  • Deep intellectual curiosity about AI systems — you read papers dig into technical details and have strong opinions about how things should be built
  • Enough customer instinct to know what matters but your default mode is building not selling
  • Bias for action and comfort wearing multiple hats in fast-moving environments
  • Willingness to travel to allied partner nations as needed

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
Machine Learning
Software Engineering
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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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Date Posted

04/10/2026

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