Staff Machine Learning Engineer, Ads ML Efficiency
Reddit · Remote · United States · 14h ago
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:
This job posting may span more than one career level.
In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/.
To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.
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