Machine Learning Engineer (Ads Optimization & Ads Marketplace Quality)

- This role sits in the Ads Optimization and Ads Marketplace Quality (AMQ) 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 - We are hiring Machine Learning Engineers (IC3 and 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) or Ads Marketplace Quality (ad matching, ad load, quality controls) to deliver measurable wins for advertisers and Redditors - We are hiring at both IC3 and IC4 levels: - IC3 MLEs are strong individual contributors who can independently own scoped projects, ship models and services, and contribute to experimentation and measurement - 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 - 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) - Marketplace Quality and Optimization: - Partner with Ads Marketplace Quality to: - Improve ad matching and ranking by incorporating new quality and relevance signals into bidding and auction decisions - Inform policies around ad load and eligibility that protect user experience while increasing high-quality ad opportunities - Collaborate closely with Ads Optimization to integrate new bid strategies and pacing mechanisms into the broader ads ecosystem and measurement stack - Potential Teams: - Ads Optimization (bid strategies, conversion/ROAS optimization, pacing and budget allocation) - Ads Marketplace Quality (ad matching, load, and quality controls) ### Benefits - Comprehensive health benefits - Flexible vacation & Reddit global days off - Family planning funds & 4+ months paid parental leave - Personal & professional development funds - Paid volunteer time off - Workspace & home office benefits- (Level will be determined during the interview process; IC4 expectations assume deeper experience and broader scope.) - Experience designing scalable data processing systems (e.g., Spark, Kafka, Airflow, BigQuery, Redis) - Degree or equivalent background in a quantitative field (math, physics, quantitative finance, economics, operations research, or similar) - Comfort reasoning about and implementing custom optimization logic (e.g., gradient-based methods, constraint handling), not just applying black-box tooling - Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals - Work experience in optimization-heavy domains (e.g., bidding/auctions, pacing, pricing, logistics optimization, quantitative finance) - Additional expectations for strong bidding/auction candidates (especially IC4): - Evidence of stronger math and optimization skills than a generic MLE, such as: - Demonstrated ability to translate ambiguous product or business problems into solutions and to improve measurable metrics - 3–5+ years of experience building, deploying, and operating machine learning systems in production (for IC4, typically 5+ years) - Bidding, pacing, or budget optimization - Experience with advertising/auction systems, online marketplaces, or search/ranking systems at scale, particularly in: - 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 (MS or PhD) in Computer Science, Machine Learning, Operations Research, Applied Math, or a related quantitative field