Machine Learning Engineer

Goliath Partners is hiring a hands-on Machine Learning Engineer in San Francisco, CA (onsite) to build and scale ML infrastructure for training, experimentation, and deployment.

Responsibilities

• Design, build, and scale infrastructure used for training large machine learning models
• Create and evolve distributed training systems with a focus on efficiency, reliability, and throughput
• Develop data pipelines and supporting infrastructure for large-scale ML workloads
• Improve GPU utilization and build supporting capabilities for compute orchestration, checkpointing, and experiment management
• Develop developer-facing tooling that helps researchers and ML engineers iterate faster
• Investigate and diagnose performance bottlenecks across the training, data, and compute systems
• Support the journey of ML systems from experimentation through production and real-world deployment

Requirements

• Strong Python and software engineering foundations
• Experience building ML infrastructure, training infrastructure, or large-scale distributed systems
• Hands-on experience with PyTorch and modern ML training stacks
• Strong understanding of distributed computing and GPU-based workloads
• Proven ability to build reliable systems that support either ML research or production ML
• Comfort working in a fast-moving environment with meaningful technical ownership

Technologies

• Python
• PyTorch

Especially Interesting 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 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

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