Machine Learning Engineer

This hands-on Machine Learning Infrastructure role at Goliath Partners focuses on building and scaling the systems that power large-scale model training, experimentation, and deployment. The work spans distributed training, data pipelines, GPU utilization, and internal tooling designed to improve iteration speed.

Key Responsibilities

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

Required Qualifications

• Strong Python skills and solid software engineering fundamentals.
• Experience building ML infrastructure, training infrastructure, or large-scale distributed systems.
• Hands-on experience with PyTorch and modern machine learning training stacks.
• Strong understanding of distributed computing and GPU-based workloads.
• Experience building reliable systems that support either ML research or production ML.
• Comfort working in a fast-moving environment with significant technical ownership.

Technologies

• Python
• PyTorch

Especially Relevant Backgrounds

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

Location and Salary

Location: San Francisco, CA (onsite)

Compensation: USD 1,000,000 - 1,000,000 per year