Senior ML Infra Engineer — Scalable Training & Serving

People Culture Talent; San Francisco (CA); On-site; USD 190,000 - 230,000; Full time

Job summary

People Culture Talent seeks an experienced ML Infrastructure Software Engineer to design, build, and operate infrastructure for ML workloads. You’ll partner with researchers and engineers to enable experimentation, training, evaluation, and production deployment with a focus on reliability and performance.

Responsibilities include creating robust pipelines, improving compute utilization, and leading architectural decisions from design to deployment.

Qualifications

• Senior-level software engineer with ownership of ML infrastructure.
• Proven design, build and operation of core ML infra.
• Hands-on PyTorch, model serving engines, and distributed training.
• Experience with distributed systems and GPU workloads.
• Background in RL post-training infra, RLHF, or automated model evaluation.
• Strong systems design, debugging and performance optimization.
• Clear communication of architectural trade-offs and decisions.
• Experience maintaining and evolving systems over time.

Responsibilities

• Design and build infrastructure for model training and production serving.
• Develop reliable pipelines and shared tools for ML workloads.
• Improve compute utilization, scheduling and performance.
• Use profiling and monitoring to identify bottlenecks and troubleshoot.
• Automate testing and measurement to assess changes.
• Lead architectural decisions through design, deployment and operation.
• Collaborate with researchers and engineers to translate requirements into systems.

Skills

Senior ownership

Distributed systems

System design & performance

Communication

End-to-end ownership

Tools

PyTorch

Model serving engines

Distributed training frameworks

GPU compute experience

Job description

People Culture Talent seeks an experienced ML Infrastructure Software Engineer to design, build, and operate infrastructure for ML workloads. You’ll partner with researchers and engineers to enable experimentation, training, evaluation, and production deployment with a focus on reliability and performance.

Responsibilities include creating robust pipelines, improving compute utilization, and leading architectural decisions from design to deployment.