Machine Learning, Recommendation Systems (Ads Infra and Ranking)
Description
Meta's Ads organization builds the systems that help businesses of every size reach the people most likely to care about what they offer. At our core, we're builders who care about shipping real value. Within Ads, the Infra and Ranking organization owns the technical foundation that makes this work at Meta's scale: the ranking models that decide which ad is right for each moment; the retrieval and serving platforms that evaluate enormous candidate sets in milliseconds; and the underlying data, training, and inference infrastructure. All of this is done while maximizing compute efficiency, ensuring reliability and privacy, and using AI to speed up development. Our state-of-the-art research in deep learning architectures, sequence and representation learning, and generative recommenders continuously pushes the boundaries of personalization accuracy. Our innovations in distributed GPU training, model/system co-design, and large-scale low-latency serving let us deploy multi-billion-parameter models that learn continuously from real-time behavior — under strict efficiency, reliability, and privacy constraints. These advances in large-scale ML optimization, ads ranking quality, and personalization infrastructure are crucial to our revenue goals and to innovation across the Meta family of apps. As an ML Software Engineer in Ads Infra and Ranking, you will build production recommendation systems that improve ad relevance through retrieval and ranking. You will own model and software improvements from design and evaluation through rollout, partnering with data and serving engineers to deliver reliable personalization capabilities and measurable improvements.
Responsibilities
Own a significant area of the ads ranking stack end to end — model architecture, features, training data, evaluation, and production rollout. Lead architecture work in deep learning for recommendations (sequence models, multi-task and multi-objective learning, transfer learning, generative recommenders). Define evaluation methodology and guardrails — offline/online correlation, calibration, holdouts, long-term and incrementality effects. Drive value-model and auction-adjacent modeling work in partnership with auction, bidding, and pacing teams. Lead GPU-scale training and serving efficiency work, including model/system co-design and inference optimization. Set technical direction for a team, drive consensus, and lead successful cross-functional partnerships. Raise engineering and modeling quality through code and design review, testing, staged rollouts, and monitoring. Own reliability and responsible-AI practices for a major ranking surface. Contribute to team and organizational growth through mentoring engineers, participating in recruiting and interviews, and identifying opportunities to improve team workflows.
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 6+ years of programming experience in a relevant programming language OR 3+ years of experience + PhD 1+ years of hands-on experience in one or more of the following areas: machine learning, recommendation systems, llms or artificial intelligence Experience with scripting languages such as Python, Javascript or Hack Experience with developing machine learning models at scale from inception to business impact Knowledge developing and debugging in C/C++ and Java, or experience with scripting languages such as Python, Perl, PHP, and/or shell scripts Track record of setting technical direction for a team, driving consensus and successful cross-functional partnerships Proven experience designing, building, or deploying recommendation systems (e.g., collaborative filtering, content-based, hybrid approaches, personalization at scale) Experience building and shipping high quality work and achieving high reliability Experience improving quality through thoughtful code reviews, appropriate testing, proper rollout, monitoring, and proactive changes Direct experience with ads ranking or monetization — auction dynamics, bidding, pacing, value modeling, or advertiser-facing optimization Experience with distributed training and large-scale low-latency model serving Master's degree or PhD in Computer Science or another ML-related field Experience with sequence modeling, multi-task/multi-objective optimization, and transfer learning for recommendations Experience with GPU scaling, model/system co-design, CUDA, Triton, or inference optimization Publications in top-tier conferences/journals, patents, or open-source contributions in the recommendations or LLM space Experience with privacy-preserving or aggregated measurement and delayed-conversion modeling
Compensation: $154,003/year to $217,000/year + bonus + equity + benefits