Software Engineer – ML Infrastructure
About The Position
We’re seeking an experienced ML Infrastructure Software Engineer to build and improve infrastructure for machine learning workloads. The role partners with researchers and engineers to support experimentation, training, evaluation, and production deployment, with a focus on reliability and performance. The engineer will design and build infrastructure for model training and production serving, develop reliable pipelines and shared tools, and improve compute utilization, workload scheduling, and performance across distributed systems. The role includes profiling and monitoring systems to identify bottlenecks, troubleshooting failures, automating testing and measurement, and guiding improvements. The position carries technical ownership from initial design through deployment and ongoing operation. It involves leading architectural decisions, explaining trade-offs, and collaborating with researchers and engineering teams to turn evolving requirements into maintainable systems.
Requirements
- Substantial software engineering experience with technical ownership at Senior Engineer level or equivalent.
- A track record of personally designing, building, and operating core machine learning infrastructure.
- Hands-on experience with PyTorch, model serving engines, and distributed training frameworks.
- Strong experience with distributed systems and large-scale compute environments, including GPU-based workloads.
- A solid background in reinforcement learning (RL) post-training infrastructure, RLHF, or automated model evaluation pipelines.
- Practical experience with modern machine learning frameworks and their supporting infrastructure.
- Strong systems design, debugging, and performance optimization skills.
- Clear, concise communication and the ability to explain contributions, architectural choices, and technical trade-offs.
- Experience owning systems over time, including maintenance, reliability, and improvements as requirements evolve.
Nice To Haves
- Experience supporting both training infrastructure and production model deployment.
- Experience with large language model workloads; infrastructure experience with other types of machine learning models is equally relevant.
Responsibilities
- Design and build infrastructure for model training and production serving.
- Develop reliable pipelines and shared tools that help teams run and manage machine learning workloads.
- Improve compute utilization, workload scheduling, and performance across distributed systems.
- Use profiling and monitoring to identify bottlenecks, troubleshoot failures, and guide improvements.
- Automate testing and measurement so teams can assess changes consistently.
- Lead architectural decisions, explain trade-offs, and carry projects from initial design through deployment and ongoing operation.
- Collaborate with researchers and engineering teams to translate evolving requirements into maintainable systems.
- Maintain, operate, and improve owned systems as requirements evolve.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
