Staff Machine Learning Engineer, ML Efficiency

About Reddit

Reddit is a community of communities built on shared interests, passion, and trust. It 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.

Location

Reddit has a flexible first workforce. 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. The team focuses 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.
• 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

• Proficiency in at least one systems language (Go, C++, Rust, or Java).
• Experience with large-scale recommendation, ranking, generative AI, or foundation model systems.
• Experience with distributed training frameworks such as PyTorch Distributed, Ray, Tensorflow, or 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

Interview Process

In select roles and locations, 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, the following categories of personal information will be collected: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share. This information will be used to evaluate your application for employment or an independent contractor role. Personal information will not be sold or disclosed to any third party for their marketing purposes. Any recording of your interview will be deleted promptly after a hiring decision is made. For more information about how personal information will be handled, including retention, please refer to the Candidate Privacy Policy for Potential Employees and Contractors.

Equal Opportunities

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