ML Infrastructure Engineer
$180,000–$250,000 year
On-site · San Francisco, California, United States
Job Summary
Join a pioneering team at Graphon as an ML Infrastructure Engineer where you'll work directly with the founding team to scale innovative Relational Systems Models. The role requires strong software engineering skills in production systems, proficiency in Python, and experience with machine learning infrastructure. Familiarity with modern ML stacks and core systems concepts is essential, along with a mindset focused on ownership and pragmatic problem-solving. Compensation includes a base salary between $180,000 and $250,000, alongside equity and comprehensive benefits.
Required Qualifications
- Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Robotics, or a related technical field (or equivalent practical experience)
- Strong software engineering fundamentals with experience building production systems (Python required; experience with C++/Go a plus)
- Experience working with machine learning infrastructure, such as training pipelines, inference/serving systems, data pipelines, or model deployment
- Familiarity with modern ML stacks (e.g., PyTorch/JAX, GPUs, distributed training or inference)
- Solid understanding of core systems concepts (version control, Linux, basic networking, CI/CD)
- High agency and ownership mindset—you identify problems, propose solutions, and drive execution
- Pragmatic, “right tool for the job” thinker who enjoys collaborating and iterating quickly
Desired Qualifications
- Experience scaling ML systems in production environments
- Experience with distributed systems, large-scale data processing, or GPU optimization
- Exposure to multimodal ML, retrieval systems, or graph-based representations
- Prior startup experience or comfort operating in ambiguous, fast-moving environments
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