Staff Product Manager Agentic Platform

Role:

Scale is at the forefront of the AI revolution working across the US government partners and allies around the world to unlock the potential of generative AI (GenAI).

We are seeking a product leader to join our team and play a pivotal role in building Agentic AI platforms to support national-level decisions including for some of the nation’s most important national security challenges. The ideal candidate will have a strong understanding of product leadership software engineering principles practices and deep experience with ML/AI application development coupled with proven experience in managing complex projects with multiple stakeholder or AI-related projects within a government or highly regulated setting emphasizing ethical AI deployment and robust risk management practices. 

This role requires a strategic leader adept at navigating the complexities of government GenAI projects ensuring Scale’s public sector AI solution aligns with agency objectives and adheres to stringent security and compliance mandates. The product manager will be responsible for the entire lifecycle of the generative AI platform including product design cross-program execution capability prioritization stakeholder engagement with various government entities defining and managing engineering scope developing detailed project plans and overseeing resource allocation and budget management. A key focus will be on ensuring that Scale’s public sector AI solution operates securely within controlled network environments and is configured properly to support government workflows specifically those that relate to national defense.

Some examples of GenAI applications we build are:

• Agentic warfare and scenario planning
• Indications and warnings integration for the protection of critical continental level assets
• Deep research capability that can help evaluate thousands of pages of classified information 
• Report generation for multiple customized report templates 
• Text2SQL intelligence applications to make analysts more efficient and embed a culture of data-driven decision-making

You will:

• Develop enterprise grade solutions that leverage cutting edge AI and AI agents to drive value for public sector customers 
• Work with executives at Scale and our customers to determine and execute the product strategy of the business.
• Own end-to-end product development by understanding customer pain points defining product requirements managing development testing and launches
• Lead cross-functional teams including engineering product design operations marketing go-to-market and finance.
• Develop a point of view and execute on turning the solutions we build into scalable software that we can commercialize across the industry 
• Maintain a Top Secret security clearance 

Ideally you'd have:

• Technical degree in computer science engineering or equivalent experience
• 4+ years of experience in building ML-powered and / or enterprise-facing products
• Strong understanding of generative AI technologies and their applications in public or large-scale private sector settings
• Experience operating in a fast-paced environment with high ambiguity
• Exceptional leadership presentation and communication skills with the ability to influence cross-functional teams
• Data literacy and experience with data analytics
• Prior military or government experience 
• Coding experience (e.g. Python)

Nice to haves:

• Experience building infrastructure and tooling to develop and support agentic applications.
• Experience working in startup environments building solutions for public sector / federal customers.
• Understanding of public / federal networks infrastructure and deployment constraints.
• TS/SCI Security Clearance

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.