I'm looking to connect with exceptional ML Infrastructure Engineers interested in opportunities with some of the most innovative teams in AI.
We're targeting engineers with deep technical expertise in building and scaling the infrastructure behind cutting-edge AI systems, from distributed model training to high-performance inference.
What We're Looking For
5+ years of experience in ML Infrastructure, Distributed Systems, or Software Engineering, or equivalent demonstrated expertise.
Strong programming skills in Python and C++ or Go.
Hands-on experience with PyTorch, JAX, or similar ML frameworks.
Experience building scalable distributed training and inference infrastructure.
Strong knowledge of Kubernetes, Docker, AWS/GCP, and GPU compute environments.
Experience optimizing system performance, reliability, latency, and throughput.
Proven ability to design and own production ML infrastructure at scale.
Education
Bachelor's or Master's degree in Computer Science, Computer Engineering, Mathematics, or a related technical field.
PhD in Computer Science, Machine Learning, or Distributed Systems is a plus.
Exceptional engineers with demonstrated technical impact and deep systems expertise are strongly encouraged to apply.
What Sets You Apart
Experience supporting large-scale LLM or foundation model training.
Expertise in CUDA, Triton, NCCL, or GPU performance optimization.
Experience with distributed training frameworks such as Ray, DeepSpeed, or Megatron-LM.
Contributions to high-performance ML systems or influential open-source infrastructure projects.
Experience building infrastructure at leading AI research labs or high-growth technology companies.
We're particularly interested in engineers who have owned complex infrastructure systems, solved difficult scaling challenges, and directly improved the performance of production ML workloads.
If you're interested in exploring confidential ML Infrastructure opportunities in San Francisco or New York, I'd love to connect.
