ML Infrastructure Engineer: Scale Ray + PyTorch Pipelines

Bonfirevc; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Orbifold AI in Palo Alto builds the foundational infrastructure that the next generation of physical AI runs on. You will scale and optimize ML infrastructure behind pipelines that process multimodal data—video, images, sensors—for world-model and robotics teams, using Ray and PyTorch.

You will own end-to-end systems, focus on performance, fault tolerance, and observability, and collaborate with research, data, and product engineers to translate modeling constraints into scalable, reliable

Qualifications

• 3+ years of software engineering experience with a focus on backend, distributed systems, or ML infrastructure.
• Strong proficiency in Python and production-grade code.
• Deep practical knowledge of PyTorch - including model serving, data loading bottlenecks, and memory management.
• Hands-on experience with Ray for scaling Python and ML applications.
• Solid understanding of distributed systems concepts: networking, concurrency, fault tolerance, parallel processing.
• Comfortable owning systems end to end in fast-paced applied research or startup environments.

Responsibilities

• Architect, build, and optimize distributed ML pipelines on Ray (Ray Core, Ray Train, Ray Serve) and PyTorch, designed for multimodal data at scale.
• Profile and tune distributed training jobs and inference deployments to maximize GPU/CPU utilization and reduce latency.
• Build robust abstractions and internal tools to deploy PyTorch models onto Ray clusters seamlessly.
• Design and maintain high-throughput video processing pipelines feeding training and evaluation workloads.
• Ensure high availability, fault tolerance, and observability of distributed compute systems.
• Collaborate with research, data, and product engineering teams to translate modeling constraints into scalable infrastructure solutions.

Skills

Python

PyTorch

Distributed systems

ML infra

Backend engineering

Research collaboration

Tools

Ray

PyTorch

Job description

Orbifold AI in Palo Alto builds the foundational infrastructure that the next generation of physical AI runs on. You will scale and optimize ML infrastructure behind pipelines that process multimodal data—video, images, sensors—for world-model and robotics teams, using Ray and PyTorch.

You will own end-to-end systems, focus on performance, fault tolerance, and observability, and collaborate with research, data, and product engineers to translate modeling constraints into scalable, reliable