Machine Learning Engineer, Applied AI Infrastructure

Orbifold AI is building foundational infrastructure for the next generation of physical AI, collaborating with leading robotics and world model research teams. They are seeking a Machine Learning Engineer to scale and optimize ML infrastructure, managing large volumes of multimodal data for advanced AI applications.

Responsibilities:

• Architect, build, and optimize distributed ML pipelines on Ray (Ray Core, Ray Train, Ray Serve) and PyTorch, designed for the demands of multimodal video, image, and sensor 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 that let our researchers and product engineers deploy PyTorch models onto our Ray clusters seamlessly
• Design and maintain high-throughput video processing pipelines (e.g. FFmpeg, NVDEC/NVENC, frame-level indexing) that feed our curation, training, and evaluation workloads
• Ensure the high availability, fault tolerance, and observability of our distributed compute systems
• Build the serving infrastructure for our evaluation harnesses, verification models, and RL environments
• Collaborate with research, data, and product engineering teams to translate modeling constraints into scalable infrastructure solutions