Machine Learning Engineer, Ads Optimization & Ads Marketplace Quality
Build and evolve auction, bidding, and budgeting ML systems that power Reddit Ads. Design optimization algorithms balancing advertiser performance, user experience, and marketplace efficiency.
About the job
Responsibilities
Auction, Bidding, and Pacing Systems
- Design and implement models and policies that compute bids for different optimization objectives (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 (ROI, revenue, delivery smoothness, fairness, user experience).
Marketplace Quality and Optimization
- 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.
- Integrate new bid strategies and pacing mechanisms into the broader ads ecosystem and measurement stack.
General
- 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).
Requirements
- 3–5+ years of experience building, deploying, and operating machine learning systems in production (5+ years for IC4).
- Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals.
- Experience designing scalable data processing systems (Spark, Kafka, Airflow, BigQuery, Redis).
- Demonstrated ability to translate ambiguous product or business problems into solutions and improve measurable metrics.
- Evidence of stronger math and optimization skills: degree or equivalent background in a quantitative field (math, physics, quantitative finance, economics, operations research).
- Work experience in optimization-heavy domains (bidding/auctions, pacing, pricing, logistics optimization, quantitative finance).
- Comfort reasoning about and implementing custom optimization logic (gradient-based methods, constraint handling).
Preferred Qualifications
- Experience with advertising/auction systems, online marketplaces, or search/ranking systems at scale.
- Experience in bidding, pacing, budget optimization, auction design, mechanism design, marketplace quality, or campaign performance optimization (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.
- Advanced degree (MS or PhD) in Computer Science, Machine Learning, Operations Research, Applied Math, or a related quantitative field.
Benefits
- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs (workspace, professional development, caregiving support)
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
Skills
Python, Java, Go, Spark, Kafka, Airflow, BigQuery, Redis, Machine Learning, Optimization Algorithms, Bidding Systems, Auction Design, Pacing, Budget Optimization
Similar jobs
ML Engineering jobs
Earnin
Mountain View, CA
Build production-grade AI agents, evaluation infrastructure, and developer tooling that make AI-assisted engineering faster, safer, and reusable across teams. The role requires software engineering experience, platform or internal developer-product experience, and hands-on expertise with LLM integration and orchestration.
Nuro
Mountain View, CABuild trustworthy infrastructure for production LLM agents, closed-loop evaluation, and autonomous research workflows. The role requires strong Python and distributed-systems experience, hands-on LLM post-training and inference knowledge, and experience operating agent systems at scale.
Mirage
New York, NY
Build and deploy agentic systems that power AI-driven creative video workflows. The role requires 5+ years of experience, production ML or agentic pipeline development, context engineering, and expertise in evaluation and agent infrastructure.
Mirage
New York, NY
Build and advance agentic machine-learning systems for multimodal creative tasks, with a focus on video understanding, reasoning, control, and tool use. The role requires strong production ML or agent-pipeline experience and deep knowledge of modern LLM techniques.
Taste Labs
San Francisco, CA
Build evaluation methods, RL environments, agent tooling, and scalable infrastructure that make subjective qualities such as design and taste measurable for frontier AI models. The role requires experience with evaluations, RL environments, ML or post-training, plus strong backend engineering skills.