Machine Learning, Recommendation Systems (Ads Infra and Ranking)

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. Minimum 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 Preferred Qualifications • 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