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# ML Infrastructure Software Engineer

- **Company:** PEOPLE INC.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Automation of Tests, Software Debugging, Distributed Computing Environment, Distributed Systems, Machine Learning, Performance Tuning, Tensorflow, Software Deployment, Software Engineering, Reinforcement Learning, Pytorch, Model Validation, Machine Learning Operations
- **Published:** November 13, 2026
- **Apply:** https://startup.jobs/software-engineer-ml-infrastructure-people-culture-talent-10757562

## About the Role

* 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.
 * 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 your contributions, architectural choices, and technical trade-offs.
 * Experience owning systems over time, including maintenance, reliability, and improvements as requirements evolve., * Experience supporting both training infrastructure and production model deployment.
 * Experience with large language model workloads is a plus, but not required. Infrastructure experience with other types of machine learning models is equally relevant.

## Description

We're seeking an experienced ML Infrastructure Software Engineer to build and improve infrastructure for machine learning workloads. You'll partner with researchers and engineers to support experimentation, training, evaluation, and production deployment, with a focus on reliability and performance., * 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 to help teams 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 turn evolving requirements into maintainable systems.