ML Infrastructure Engineer (AI)

Location: San Francisco (On-site)

Company

hirify.global is developing foundation models and learning algorithms to power general-purpose robots and physically-actuated devices.

What you will do

• Design and maintain systems for large-scale model training, including scheduling, job management, and logging.
• Scale JAX-based training across TPU and GPU clusters.
• Profile and improve memory usage, device utilization, and throughput.
• Build abstractions for launching, monitoring, and debugging experiments.
• Partner with researchers to translate needs into infrastructure capabilities.
• Evolve core training code to support new architectures and modalities.

Requirements

• Strong software engineering fundamentals and experience building ML training infrastructure.
• Hands-on experience with large-scale training in JAX or PyTorch.
• Familiarity with distributed training, multi-host setups, and data pipelines.
• Experience managing workloads on cloud platforms like Kubernetes, GCP, or AWS.
• Ability to debug and optimize performance bottlenecks across the training stack.
• Must be able to work on-site in San Francisco.

Nice to have

• Deep ML systems background (compilers, runtime optimization, custom kernels).
• Experience with GPU/TPU performance tuning.
• Background in robotics or multimodal foundation models.

Culture & Benefits

• Work at the intersection of ML, software engineering, and robotics.
• High-leverage role impacting core modeling efforts.
• Collaborative environment working closely with researchers and platform engineers.