Software Engineer – ML Infrastructure

San Francisco, CAfull time

On Site

$150k–$350k

H1B: no

People Culture Talent Software Engineer – ML Infrastructure builds and improves infrastructure for machine learning workloads, focusing on reliability and performance. The role includes designing infrastructure for model training and production serving and collaborating with researchers and engineers.

Responsibilities

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.
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 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.
What You’ll Bring
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

Qualification

Track record of designingHands-on experience with PyTorchStrong systems design

Required

Substantial software engineering experience with technical ownership at Senior Engineer level or equivalent
Track record of 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 post-training infrastructure, RLHF, or automated model evaluation pipelines
Practical experience with modern machine learning frameworks and supporting infrastructure
Strong systems design, debugging, and performance optimization skills
Experience owning systems over time including maintenance, reliability, and improvements

Preferred

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.