Staff Machine Learning Engineer, ML Efficiency

Reddit; Netherlands; On-site; EUR 70,000 - 90,000; Full time

Benefits offered by this job

Comprehensive health benefits

Flexible vacation

Parental leave

Job summary

Reddit is looking for a seasoned engineer to join the ML Efficiency team in the Netherlands. This role involves building systems aimed at enhancing the efficiency of machine learning training and inference, ensuring improved performance while reducing costs.

Successful candidates will need over 5 years of software engineering experience, proficiency in Python, and a solid understanding of performance engineering. Benefits include comprehensive health options and generous parental leave.

Qualifications

• 5+ years of software engineering experience.
• Strong proficiency in Python.
• Experience with large-scale recommendation and AI systems.

Responsibilities

• Design and build systems to optimize ML workloads.
• Develop tooling for debugging, profiling, and monitoring models.
• Lead initiatives to improve ML platform scalability.

Skills

Software engineering experience

Python proficiency

Performance engineering

Debugging and profiling skills

Distributed systems

Education

BS, MS, or PhD in Computer Science or related field

Tools

PyTorch Distributed

TensorFlow

Ray

Spark

Job description

• 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 & professional development funds
• Paid volunteer time off
• Workspace & home office benefits

Qualifications

• 5+ years of software engineering experience
• Strong proficiency in Python
• Proficiency in at least one systems language (Go, C++, Rust, or Java) preferred
• Deep understanding of performance engineering and systems optimization
• Experience with machine learning infrastructure, training systems, or model serving platforms
• Strong debugging and profiling skills
• Experience building distributed systems at scale
• BS, MS, or PhD in Computer Science or a related field
• 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