Greenhouse ·
full-timeStaff Machine Learning Engineer, Ads ML Efficiency
Reddit · Remote - United States
Reddit operates a platform that hosts over 100,000 active communities where users submit, vote, and comment on topics they care about, serving roughly 130 million daily active visitors. The ML Efficiency team within Reddit builds the infrastructure, tooling, and optimization systems that allow machine‑learning engineers and researchers to train, evaluate, deploy, and operate models efficiently at scale. As a Staff Machine Learning Engineer focused on Ads ML Efficiency, you would design and build systems that improve the efficiency of ML training and inference workloads, develop tooling for debugging, profiling, and monitoring model performance, and work to increase GPU and overall resource utilization through better scheduling, caching, and workload optimization. You would partner with ML researchers and product teams to identify bottlenecks, create benchmarking frameworks and performance dashboards, optimize distributed training pipelines and model‑serving architectures, and lead cross‑functional initiatives that raise the productivity of Reddit’s ML engineers while driving technical strategy for platform scalability, reliability, and cost efficiency. The role suits candidates with a strong software‑engineering background, several years of experience building large‑scale distributed systems, deep Python proficiency, familiarity with a systems language such as Go, C++, Rust or Java, and a track record of performance engineering and ML‑infrastructure work.
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 . Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the US or Canada. About the Team The ML Efficiency team builds the infrastructure, tooling, and optimization systems that enable machine learning engineers and researchers to train, evaluate, deploy, and operate models efficiently at scale. We focus on improving developer productivity, reducing infrastructure costs, increasing hardware utilization, and accelerating experimentation across the company’s ML ecosystem. Responsibilities Design and build systems that improve the efficiency of ML training and inference workloads. Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance. Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization. Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements. Build benchmarking frameworks and performance dashboards for training and serving systems. Optimize distributed training infrastructure, data pipelines, and model serving architectures. Lead cross-functional initiatives that improve the productivity of Reddit ML engineers. Drive technical strategy for ML platform scalability, reliability, and cost efficiency. Qualifications Required BS, MS, or PhD in Computer Science or a related field. 5+ years of software engineering experience. Strong proficiency in Python Profiency in at least one systems language (Go, C++, Rust, or Java) preferred Experience building distributed systems at scale. Experience with machine learning infrastructure, training systems, or model serving platforms. Deep understanding of performance engineering and systems optimization. Strong debugging and profiling skills. Preferred Experience with large-scale recommendation, ranking, generative AI, or foundation model systems. Experience with distributed training frameworks such as PyTorch Distributed, Ray, Tensorflow, Spark Familiarity with GPU architectures and performance analysis tools. Experience optimizing cloud infrastructure costs across large ML workloads. Contributions to internal platforms used by multiple ML teams. Experience with building real time ML inference applications What Success Looks Like ML engineers can move from idea to experiment faster. Training and inference costs decrease, performance increases, while model quality is maintained or improved. GPU utilization and cluster efficiency increase. Platform reliability improves as ML workloads scale. Teams spend less time managing infrastructure and more time building models. Average recommendation model size increases. Benefits: Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Group Personal Pension Scheme with Employer match Private Medical and Dental Scheme Income Replacement Programs Bike to Work scheme Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $230,000 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professio
| Role | Staff Machine Learning Engineer, Ads ML Efficiency |
|---|---|
| Company | |
| Location | Remote - United States |
| Type | internship |
| Compensation | Not disclosed |
| Posted | 2026-10-01 |
| Deadline | Rolling |
Typical process for this type of role
A general guide — the exact steps for this specific listing may vary; check the original posting for details.
- 1ApplicationSubmit your resume through the apply link.
- 2ScreeningRecruiter reviews your background against the role.
- 3AssessmentA technical test, assignment, or coding round, depending on the role.
- 4Interview(s)One or more rounds with the hiring team.
- 5OfferOffer letter with compensation and start date.
Before you apply
0/4Staff Machine Learning Engineer, Ads ML Efficiency at Reddit: frequently asked questions
- What is the salary for this role?
- Reddit has not stated compensation in the listing. Check the original posting or ask during the application process.
- Is the Staff Machine Learning Engineer, Ads ML Efficiency position remote, hybrid or onsite?
- The listing marks this role as remote, with Remote - United States as the location.
- What is the application deadline?
- Reddit has not listed a fixed deadline, so apply early in case the opening is filled.
- How do I apply for the Staff Machine Learning Engineer, Ads ML Efficiency role?
- Use the Apply button on this page. It opens the original listing on job-boards.greenhouse.io, where you submit your application with the company.
More at Reddit
Other jobs at Reddit
- Staff Machine Learning Engineer, Ads Creative Effectiveness · Remote - United States
- Strategic Finance Senior Manager · Remote - United States
- Chief Of Staff, Global Sales · New York City, NY
- Senior Product Manager, Notifications · Remote - United States
- Revenue Strategy & Operations Partner · New York City, NY
Explore Related Placements
// similar opportunities
You might also like
Staff Machine Learning Engineer, Ads Creative Effectiveness
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 intern...
Staff Machine Learning Engineer, App Ads Modeling
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 intern...
Senior Staff Machine Learning Engineer, Ads Ranking
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 intern...
['100% remote', '9+ years experience required']
Senior Staff Machine Learning Engineer, ML Understanding
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 intern...
Senior Staff Machine Learning Systems Engineer, Ads ML Platform
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 intern...
Staff Machine Learning Engineer
Servicenow
No description available.