Machine Learning Engineer, Ads Optimization

Team Description

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.

Role Description

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.

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).