ML Infra Engineer — Ray + PyTorch Pipelines

Orbifold AI; Palo Alto (CA); On-site; USD 170,000 - 230,000; Full time

Job summary

Orbifold AI in Palo Alto, CA is seeking a Machine Learning Engineer to scale and optimize the ML infrastructure behind our multimodal data pipelines. You will work with PyTorch and Ray to convert raw video, image, and sensor data into training, evaluation, and RL signals for frontier robotics teams.

This role emphasizes building scalable, fault-tolerant systems and delivering end-to-end infrastructure for researchers and product engineers in a fast-paced startup environment.

Qualifications

• 3+ years of software engineering experience in backend, distributed systems, or ML infrastructure.
• Strong proficiency in Python and production-grade code.
• Deep practical knowledge of PyTorch, including model serving and memory management.

Responsibilities

• Architect, build, and optimize distributed ML pipelines on Ray and PyTorch for multimodal data at scale.
• Profile and tune distributed training jobs and inference deployments to maximize GPU/CPU utilization and reduce latency.
• Build abstractions and internal tools to deploy PyTorch models on Ray clusters seamlessly.

Skills

Python

PyTorch

Ray

Distributed systems

Backend development

ML infrastructure

Production-grade code

Tools

Kubernetes

Docker

FFmpeg

NVDEC/NVENC

CUDA

C++

Job description

Orbifold AI in Palo Alto, CA is seeking a Machine Learning Engineer to scale and optimize the ML infrastructure behind our multimodal data pipelines. You will work with PyTorch and Ray to convert raw video, image, and sensor data into training, evaluation, and RL signals for frontier robotics teams.

This role emphasizes building scalable, fault-tolerant systems and delivering end-to-end infrastructure for researchers and product engineers in a fast-paced startup environment.