Senior Machine Learning Engineer, Ads Optimization

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Team Description This role sits in the Ads Optimization organizations, which are responsible for the health and performance of Reddit’s ads marketplace. We focus on: Designing the auction and bidding mechanisms that decide which ads show to which users and at what price. Building optimization systems that help advertisers achieve their goals (e.g., conversions, ROAS) under budget and delivery constraints. Ensuring marketplace quality by improving user experience with ads, fighting ad blindness, and increasing valuable ad opportunities on the platform. You’ll join a set of tight-knit engineers working on high-impact, internet-scale problems at the core of Reddit’s revenue engine, collaborating closely with Product, Data Science, and Infra partners across Reddit Ads. Role Description We are hiring Machine Learning Engineers (IC4) to build and evolve the auction, bidding and budgeting systems that power Reddit Ads. In this role, you will: Design and implement optimization algorithms for auctions, bidding strategies, and pacing that balance advertiser performance, user experience, and marketplace efficiency. Own systems end-to-end: from problem formulation and algorithm design to experimentation, production deployment, and ongoing iteration. Work across Ads Optimization (bid strategies, budget optimization, pacing) to deliver measurable wins for advertisers and Redditors. We are hiring a Senior (IC4) level: IC4 MLEs lead more complex or multi-quarter initiatives, set technical direction for key parts of the bidding/auction/pacing stack, and mentor other engineers while remaining hands-on. Responsibilities Auction, Bidding, and Pacing Systems Design and implement models and policies that: Compute bids for different optimization objectives (e.g., CPC, CPA, ROAS-based strategies). Pace budgets smoothly over time across accounts, campaigns, and ad groups while preventing overspend or underspend. Allocate spend and auction participation intelligently across segments, surfaces, and time zones. Translate product and marketplace goals into concrete optimization problems and constraints (e.g., ROI, revenue, delivery smoothness, fairness, and user experience). Required Qualifications (Level will be determined during the interview process; IC4 expectations assume deeper experience and broader scope.) 3–5+ years of experience building, deploying, and operating machine learning systems in production (for IC4, typically 5+ years). Strong programming skills in Python , Java , Go , or similar languages, with solid software engineering fundamentals. Experience designing scalable data processing systems (e.g., Spark, Kafka, Airflow, BigQuery, Redis). Demonstrated ability to translate ambiguous product or business problems into solutions and to improve measurable metrics. Additional expectations for strong bidding/auction candidates: Evidence of stronger math and optimization skills than a generic MLE, such as: Degree or equivalent background in a quantitative field (math, physics, quantitative finance, economics, operations research, or similar). Work experience in optimization-heavy domains (e.g., bidding/auctions, pacing, pricing, logistics optimization, quantitative finance). Comfort reasoning about and implementing custom optimization logic (e.g., gradient-based methods, constraint handling), not just applying black-box tooling. Preferred Qualifications Experience with advertising/auction systems , online marketplaces, or search/ranking systems at scale, particularly in: Bidding, pacing, or budget optimization Auction design, mechanism design, or marketplace quality Campaign performance optimization (e.g., CTR/CVR, CPA, ROAS) Familiarity with large-scale, real-time decision systems and low-latency production environments. Background in feature engineering, model optimization, and production monitoring for ML systems. Experience collaborating with cross-functional partners (Product, DS, Eng) in Ads or marketplace contexts and leading projects from design through rollout. Advanced degree (M