Ads ML Developer Experience Tech Lead

Meta·Sunnyvale, California, United States·posted 10d ago · last seen 44m ago

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About interviewing at Meta

Recruiter screen, a technical screen (60-minute asynchronous challenge or paired live coding), a short culture-fit questionnaire, then a virtual onsite of about four rounds: fast-paced coding with multiple problems per round, system design scoped to level, a lighter behavioral, and an AI-enabled coding round assessing how you work with a coding assistant.

Read the full Meta interview process →

Description

About

As part of the Ads AI Infra organization, our organization develops state-of-the-art AI/ML training technologies, enabling ML engineers to train, scale, and productionize cross-cutting ML model techniques. We are an engineering team with a strong hybrid ML and infrastructure skill set, dedicated to solving cross-layer and end-to-end ML infrastructure problems in the ads stack. A key focus in this area is to improve velocity and efficiency for one of our most critical resources: machine learning engineers. This is a focus for the Ads-wide AI Modeling Velocity program. We are looking for a Senior TL who is committed to being the champion for Developer Voice and driving the latest advancements in of large scale AI and ML infrastructure – you are excited about the transformative changes AI Agents will bring to software development!

Responsibilities

  • Drive the technical direction and strategy of AI Developer Velocity efforts across the Ads Ranking AI organization
  • Architects and evolve AI agent systems for model architecture research and development, including model, feature authoring agents and ML development ecosystem assistants
  • Lead development of state-of-the-art AI/ML training technologies that enable ML engineers to train, scale, and productionize cross-cutting ML model techniques
  • Champion Developer Voice by building tools and infrastructure that improve velocity and efficiency for ML engineers across the organization
  • Define big-picture strategy for developer experience improvements and operationalize it into tactical execution plans
  • Undertake ambitious technical projects in AI/ML infrastructure, compilers, build systems, and large-scale system design
  • Mentor and lead engineers across the organization in ML infrastructure and developer tooling
  • Partner with engineering managers and cross-functional partners to align on priorities and drive execution
  • Present complex technical details in an intuitive and understandable way to leadership

Minimum Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Deep and broad software engineering experience with a focus on machine learning infrastructure, developer tooling, or large-scale AI platform development
  • Experience architecting and shipping AI infrastructure at scale that serves as a multiplier for other teams to drive impact
  • Track record of defining technical strategy and driving cross-organizational alignment in developer experience or ML infrastructure
  • Experience leading and mentoring engineers on complex technical projects
  • Experience with large-scale system design, compilers, or build systems Experience with PyTorch or similar ML frameworks at production scale
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Track record of data-driven innovation in spaces where there are no existing clear answers
  • Experience with AI agents, developer productivity tools, or machine learning platforms
  • Experience driving adoption of relatively immature technologies to maturity and greater impact
  • Contributions to developer tooling, ML infrastructure open-source projects, or published work in related areas
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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