ML Infra Engineer: Scale & Optimize Large-Scale Training

Goliath Partners Inc.; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Goliath Partners Inc. in San Francisco is hiring an ML Infrastructure Engineer to build the infrastructure for training, experimentation, and deployment of large-scale models.

You will own challenging ML systems work at the intersection of machine learning, distributed systems, and infrastructure, collaborating with researchers and engineers to push production-ready capabilities. Expect to optimize GPU utilization, design data pipelines, develop tooling, and improve experiment management as you

Qualifications

• Strong Python and software engineering fundamentals.
• Experience with PyTorch and modern ML training stacks.
• Strong understanding of distributed computing and GPU-based workloads.
• Experience building reliable systems for ML research or production.
• Comfortable in a fast-moving environment with technical ownership.

Responsibilities

• Build and scale infrastructure for training large machine learning models.
• Develop distributed training systems and improve training efficiency, reliability, and throughput.
• Build data pipelines and infrastructure supporting large-scale ML workloads.
• Improve GPU utilization, compute orchestration, checkpointing, and experiment management.
• Develop tooling that enables researchers and ML engineers to iterate faster.
• Diagnose performance bottlenecks across training, data, and compute systems.
• Help take ML systems from experimentation through production and real-world deployment.

Skills

Strong Python

Distributed computing understanding

System design fundamentals

Experience with ML pipelines

Ownership in fast-moving environ

Tools

PyTorch

CUDA

Kubernetes

Job description

Goliath Partners Inc. in San Francisco is hiring an ML Infrastructure Engineer to build the infrastructure for training, experimentation, and deployment of large-scale models.

You will own challenging ML systems work at the intersection of machine learning, distributed systems, and infrastructure, collaborating with researchers and engineers to push production-ready capabilities. Expect to optimize GPU utilization, design data pipelines, develop tooling, and improve experiment management as you