Tech Stack
Responsibilities
- Architect pipelines for massive multi-GPU inference jobs on foundation-scale video models, optimizing for throughput, cost, and reliability.
- Build direct integrations with AWS S3, GCP Storage, and Azure Blobs for large-scale ingest via signed URLs and resumable uploads.
- Design event-driven, autoscaling job systems using Kubernetes, Pub/Sub, or Ray for analyzing terabytes of video data in parallel.
- Power the NomadicML Python SDK for programmatic video ingestion, analysis, and search.
- Build logging, tracing, and metrics pipelines for end-to-end observability of GPU utilization, job latency, and inference health.
Benefits
- Health Insurance
Culture
Impact-OrientedStartup Energy
Requirements
Regions: Us
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About pear-vc
Industry: ai/ml, autonomous driving, robotics
Size: startup
NomadicML is building a platform that uses Vision-Language Models (VLMs) to transform raw video footage into structured intelligence for autonomy and robotics, partnering with industry leaders.
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