Job description

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! 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 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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Keywords to mirror

aimlmachine learning

Platform: meta
Effort: medium