Reddit, Inc.
Staff Machine Learning Engineer, ML Efficiency

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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 126 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 UK or the Netherlands.
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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


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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.
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Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.
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