Machine Learning Engineer

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

I’m working with a rapidly growing, well-funded AI company building some of the most technically ambitious real-world AI systems today.

They’re looking for an ML Infrastructure Engineer to join a highly technical team responsible for building the infrastructure that powers large-scale model training, experimentation, and deployment.

This is a hands-on engineering role for someone who enjoys solving difficult systems problems at the intersection of machine learning, distributed systems, and infrastructure.

What you’ll work on:

• 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

What they’re looking for:

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

Especially interesting backgrounds include:

• Distributed training and large-scale model training
• GPU infrastructure and compute orchestration
• Large-scale data infrastructure and pipelines
• Performance optimization for ML workloads
• Infrastructure supporting robotics, embodied AI, computer vision, or other real-world ML systems
• Experience taking ML systems beyond research and into production or physical-world environments

This is a great opportunity for an engineer who wants to work on hard ML systems problems at scale while being much closer to the models and real-world applications than you would be on a traditional infrastructure team.