Reddit, Inc.
Machine Learning Systems Engineer, Ads ML Platform

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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.
Team Overview
We're building a scalable feature platform that powers Ads ML by making high-quality features and training datasets easy to build, share, and maintain. Our small but growing team works on projects like batch & realtime feature management platform, training set generation platform, sequence features platform and, agentic and automated ML workflows for feature lifecycle management.
We are looking for an engineer with experience in building high-scale data infrastructure and exposure to ML platforms to help evolve and scale our feature management systems.
This is not a pure ML modeling role. The ideal candidate is excited about building reliable infrastructure, data pipelines, and developer-facing tools that make ML engineers more productive.
What You'll Do
- Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage.
- Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use.
- Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning
- Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems.
- Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation and feature lifecycle management
- Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives.
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.
What You Bring
- 3+ years in data infrastructure/platform engineering or ML infrastructure platforms.
- Hands-on experience building production services, data pipelines, APIs, workflow systems, or developer tools.
- Experience with at least one distributed data or compute system such as Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery, or similar technologies.
- Familiarity with ML data workflows such as feature generation, training dataset creation, batch processing, real-time data processing, model training, experimentation, or online serving.
- Strong coding skills and ability to write clean, maintainable, well-tested code.
- Experience building intelligent automation or agentic workflows for ML systems is a strong plus
- Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus
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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