Engineering Manager, Agent Data

1 Day ago • 5 Years + • $212,000 PA - $254,400 PA

Job Summary

Job Description

As an Engineering Manager, you will lead the team of engineers and collaborate with customers, internal ML researchers, and operators to define, build, and support data products for Agent Data. Your responsibilities include managing the engineering team, driving technical delivery, and ensuring high-quality product delivery, from conceptualization to production. You will build features end-to-end, influence team culture, and mentor less experienced engineers. The role involves working in a fast-paced environment, turning business ideas into engineering solutions, and demonstrating excellent problem-solving skills. The Agent Data team focuses on the entire lifecycle of human data input for agents, including data architecture, tooling development, operational scaling, performance evaluation, and testing environments.
Must have:
  • 5+ years of relevant experience; 2+ years of managing engineers preferred
  • Experience in Agent-related products or research
  • Proven track record of shipping high-quality products at scale
  • Ability to translate business and product ideas into engineering solutions
  • Excellent problem-solving skills and ability to work independently/as a team

Job Details

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the development of AI modalities to pave the road to Artificial General Intelligence (AGI).

Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

 

About the role:

You will work as part of our Frontier Data team to build data to enhance LLM agent capabilities. The team partners closely with leading-edge research and development efforts from AI labs to scale up human data in new agent environments.  Data products we currently support include agents focused on software engineering, computer use, and multiple-faceted tool use.

 

The Agent Data team supports the entire lifecycle of human data input for agents:

  • Initial data architecture, prototyping, and definition
  • Development of collection tooling, quality control, and management
  • Operational scaling across thousands of expert human data collectors
  • Performance evaluation frameworks to prove data efficacy
  • Testing environments and benchmarking

 

As  Engineering Manager, you will manage the team of engineers and work closely with our customers, internal ML researchers, and operators to define, build, and support these data products.

 

Responsibilities:

  • Manage the engineering team and drive technical delivery for Agents Data
  • Deliver experiments at a high velocity and level of quality to engage our customers
  • Work across the entire product lifecycle from conceptualization through production
  • Build features end-to-end: front-end, back-end, system design, debugging and testing
  • Influence the culture, values, and processes of a growing engineering team
  • Inspire and mentor less experienced engineers

Requirements:

  • At least 5 years of relevant experience and at least 2 years of experience managing engineers is preferred
  • Experience working on Agent-related products or research
  • Track record of shipping high-quality products and features at scale
  • Desire to work in a very fast-paced environment
  • Ability to turn business and product ideas into engineering solutions
  • Excellent problem-solving skills, and be able to work independently or as part of a team.
  • Excited to join a dynamic, hybrid team in either San Francisco or New York City



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