Staff Machine Learning Engineer, Ads ML Efficiency
About this role
Engineers who thrive on scaling complex machine learning infrastructure and solving deep performance bottlenecks will find a natural fit within this group. The team constructs the core tools, optimization systems, and deployment infrastructure that allow engineers and researchers to train, evaluate, and operate models efficiently. Daily responsibilities center on designing distributed training architectures, improving hardware utilization, and building profiling tooling that accelerates experimentation across the entire organization.
This role exists to scale the platform's reliability and cost efficiency while reducing the time practitioners spend managing underlying infrastructure. Success means that internal teams can transition from ideas to experiments rapidly while driving down operational expenses and maintaining high model quality.
What you will do
- Design and build systems that improve the efficiency of machine learning training and inference workloads.
- Develop tooling that helps engineers debug, profile, optimize, and monitor model performance.
- Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization.
- Partner with machine learning 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 machine learning engineers.
- Drive technical strategy for platform scalability, reliability, and cost efficiency.
What you need
- Hold a Bachelor's, Master's, or PhD degree in Computer Science or a related field.
- Bring at least five years of software engineering experience to the position.
- Demonstrate strong proficiency in Python and familiarity with systems languages such as Go, C++, Rust, or Java.
- Show proven experience in building distributed systems at scale.
- Possess hands-on background with machine learning infrastructure, training systems, or model serving platforms.
- Display a deep understanding of performance engineering and systems optimization along with strong debugging skills.
Nice to have
- 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
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
- Flexible Vacation & Paid Volunteer Time Off
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
Reddit operates with a flexible first workforce. Employees can work remotely from anywhere in the US or Canada if they do not live near an office.
What compensation and equity are offered?
The base salary range for this position is $230,000 to $322,000 USD. In addition to base salary, the role is eligible to receive equity in the form of restricted stock units and may be eligible for a commission depending on the position.
What benefits are provided for U.S.-based employees?
U.S.-based employees receive medical, dental, and vision insurance, a 401(k) program with an employer match, and generous time off for vacation and parental leave. Additional offerings include family planning support, gender-affirming care, and mental health and coaching benefits.
How is final pay determined?
Final offer amounts are determined by multiple factors including skills, depth of work experience, and relevant licenses or credentials. Standard base pay ranges are set based on function, level, and country location, benchmarked against similar stage growth companies.