ML Infrastructure Engineer

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.

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. Compensation & Benefits Base salary range: $180,000 – $250,000 (depending on experience and level) Meaningful equity with the opportunity to own a real stake in a category-defining company Comprehensive benefits , including health, dental, vision, and competitive paid time off

Base salary range: $180,000 – $250,000 (depending on experience and level) Meaningful equity with the opportunity to own a real stake in a category-defining company Comprehensive benefits , including health, dental, vision, and competitive paid time off

Base salary range: $180,000 – $250,000 (depending on experience and level)

Meaningful equity with the opportunity to own a real stake in a category-defining company