Who you are

- 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

- 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 the job involves

- 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

- 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

- 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

- Comprehensive health benefits
- Flexible vacation & Reddit global days off

- Family planning funds & 4+ months paid parental leave

- Personal & career development funds

- Paid volunteer time off
- Workspace & home office benefits

📌 Staff Machine Learning Engineer (Canada)
🏢 Reddit
📍 Canada