Fairplace
arrow_back Torna alla ricerca
R

Staff Machine Learning Engineer, ML Efficiency

Reddit

Condividi:
place— home_workIn sede labelAds Engineering publicOfferta aggregata · DE

eventPubblicata il 06 ago 2026 · verifiedAbbiamo verificato il 27 ago 2026 che è ancora attiva

Sull'offerta

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 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.

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 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, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.

We will not sell your personal information or disclose it to any third party for their marketing purposes.

We will delete any recording of your interview promptly after making a hiring decision.

For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . 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. Find Jobs in United Kingdom on Arbeitnow

Continua a leggere gratis

Crea un account gratuito per vedere l'offerta completa e candidarti.

  • badgePortfolio visibile alle aziende
  • notificationsAvviso di nuova offerta via e-mail
  • favoriteSempre gratis, senza trucchi
Crea account gratuito Ho già un account

Offerte correlate

Web Engineer Intern

Epicgames

placeLondon 29 ago

Native Processing Server Architect (m/f/d)

Q.ANT GmbH

placeStuttgart 29 ago

Forward Deployed Engineer

Lumaai

placeBerlin, GER 29 ago

Director of Go-to-Market Engineering (Revenue Operations)

Perk

placeEdinburgh 29 ago

Director of Go-to-Market Engineering (Revenue Operations)

Perk

placeBirmingham 29 ago

Research Engineer - Inference

ElevenLabs

placeUnited Kingdom 29 ago