Staff Machine Learning Engineer, Retrieval
About this role
To handle large-scale ads delivery at Reddit, the Ads Retrieval ML team needs to scale candidate generation across millions of daily active users and thousands of active communities. This Staff Machine Learning Engineer role exists to provide technical leadership for the retrieval team, ensuring models effectively identify relevant advertising candidates before downstream ranking and auction decisions take place.
The person in this role will design, develop, and launch retrieval models using representation learning, nearest-neighbor search, and deep learning techniques. Success in this position requires a strong blend of hands-on modeling depth and technical leadership, as the engineer will stay close to technical details while setting the multi-year roadmap and mentoring other team members.
What you will do
- Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders.
- Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces.
- Work with approximate nearest-neighbor and vector retrieval systems, reasoning about trade-offs in recall, relevance, freshness, diversity, coverage, latency, and cost.
- Establish strong evaluation practices that connect offline retrieval metrics to downstream ads and user outcomes.
- Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into subsequent modeling iterations.
- Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel.
- Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership.
- Mentor machine learning engineers and help grow the team’s expertise in retrieval, recommendation, and representation learning.
What you need
- 7+ years of industry experience, including substantial experience building and shipping applied machine learning products.
- Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems.
- Strong understanding of retrieval modeling concepts, including deep neural networks, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval.
- Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks.
- Demonstrated ownership of machine learning projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration.
- Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics.
- Strong software engineering fundamentals and the ability to write clear, reliable, and maintainable production code.
- Technical leadership experience involving setting direction, leading complex projects, influencing partner teams, and mentoring other engineers.
Nice to have
- Experience with ads retrieval, ad serving, recommendation, search relevance, or marketplace optimization
- Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals
- Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experience outcomes
- Experience in ads marketplaces at peer companies
- Publications, patents, or industry contributions in applied machine learning or ranking systems
Benefits
- 100% remote opportunity with optional hybrid or onsite work in NY, SF, LA, and Chicago
- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs ranging from workspace support to professional development and caregiving support
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
This is a 100% remote opportunity. However, the company also maintains office locations for hybrid or onsite work preferences in New York, San Francisco, Los Angeles, and Chicago.
What is the compensation range for this position?
The base salary range for this position is $230,000 to $322,000 USD. In addition to base salary, the role is eligible to receive equity in the form of restricted stock units and standard U.S.-based benefits.
What tools and frameworks are used on the team?
The engineering team works with deep learning frameworks such as TensorFlow and PyTorch. They also build and maintain approximate nearest-neighbor and vector retrieval systems.
What kind of team will I be working with?
You will join the Ads Retrieval ML team, which builds machine learning systems that identify relevant advertising candidates. You will partner closely with engineering, product, data science, and ads stakeholders across the full ads funnel.