SF $1M Permanent
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 building ML infrastructure, training infrastructure, or large-scale distributed systems
- 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
- ML platforms / 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
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.
Bay Area - in person
If you have a strong ML infrastructure or distributed systems background and want to hear more, apply directly or message me.