Join Reddit as a Staff Machine Learning Engineer to enhance ML efficiency and drive performance improvements in a fully remote role.
Posted by employer 14 hours ago
First seen on Joblaze 4 hours ago
Last verified on the company career page 4 hours ago
What you'll build
- 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 and resource management
- Build benchmarking frameworks and performance dashboards for training and serving systems
- Drive technical strategy for ML platform scalability and cost efficiency
Must have
- 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
- Deep understanding of performance engineering and systems optimization
Nice to have
- Experience with large-scale recommendation or ranking systems
- Experience with distributed training frameworks such as PyTorch Distributed
- Familiarity with GPU architectures and performance analysis tools
- Experience optimizing cloud infrastructure costs across large ML workloads
- Experience with building real-time ML inference applications
Requirements
- Experience
- 5+ years
- Education
- Bachelor's degree
Not disclosed in this posting: visa sponsorship.
Benefits
Joblaze summary
The Staff Machine Learning Engineer for Ads ML Efficiency at Reddit focuses on enhancing the efficiency of machine learning training and inference systems. This role requires strong skills in Python and a systems programming language, along with experience in building distributed systems and optimizing ML infrastructure. It is suited for seasoned engineers with a background in performance engineering and systems optimization, particularly those familiar with large-scale ML applications. The position is part of a team dedicated to improving developer productivity and resource utilization across Reddit's ML ecosystem.
Joblaze insights
- Listed today — first seen on Joblaze October 2, 2026. Last confirmed on Reddit's careers page October 2, 2026.
- Salary band is above the typical range for AI/ML roles (median ~$190,000).
- Starts above 51% of 106 comparable staff ai/ml roles in United States that list Python we track (median $226,000 across 44 companies). See Python salary trends
- Python appears in 52.7% of 264 comparable staff ai/ml roles in United States; Spark appears in 3.4% of 264 comparable staff ai/ml roles in United States.
Quick facts
- Is the Staff Machine Learning Engineer, Ads ML Efficiency role remote?
- Yes — Reddit lists this as a fully remote position.
- What's the salary range?
- Reddit lists $230,000–$322,000 for this role.
- How much experience is required?
- At least 5 years of relevant experience for this Staff Machine Learning Engineer, Ads ML Efficiency role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: C++, Go, Java, PyTorch, Python, Ray.
- What seniority level is this role?
- Reddit targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Staff Machine Learning Engineer, Ads ML Efficiency role at Reddit.
From the original posting
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:
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.
Standard company text repeated across Reddit's postings is omitted here.