ML Platform Engineer

Tech Stack

Responsibilities

• Design, deploy, and operate production inference infrastructure — including model serving, autoscaling, load balancing, and cost optimization across cloud environments
• Own the platform architecture for embedding and retrieval pipelines that power semantic search over multimodal robotics data (image, video, point cloud, and timeseries)
• Build and maintain the training and evaluation infrastructure that enables rapid iteration on model performance — including job orchestration, experiment tracking, and dataset versioning
• Drive cloud infrastructure decisions (AWS/GCP) that directly impact latency, throughput, reliability, and cost at scale
• Define platform abstractions and internal tooling that let product engineers ship ML-powered features without needing to manage infrastructure themselves

Benefits

• 401k
• Equity
• Health Insurance
• Remote Work

Culture