Member of Technical Staff, Backend - NomadicML

About the Role

We’re looking for a Backend / Infrastructure Engineer who thrives at the intersection of cloud systems, SDK design, and large-scale inference infrastructure.

You’ll build and scale the backbone that powers NomadicML’s video intelligence platform - from secure cloud ingestion to distributed GPU inference pipelines that run our largest foundation models. You’ll collaborate with ML researchers to productionize their models, automate deployment and scaling, and expose those capabilities through clean APIs and SDKs used by enterprises worldwide.

This role blends systems engineering, distributed compute orchestration, and developer experience. You’ll be working across cloud storage, inference scheduling, GPU clusters, and the NomadicML SDK.

What You’ll Build

• GPU Inference Workflows: Architect pipelines to run massive multi-GPU inference jobs on foundation-scale video models, optimizing for throughput, cost, and reliability.
• Cloud Upload Infrastructure: Build direct integrations with AWS S3, GCP Storage, and Azure Blobs to support large-scale ingest via signed URLs and resumable uploads.
• Distributed Processing Pipelines: Design event-driven, autoscaling job systems using Kubernetes, Pub/Sub, or Ray for analyzing terabytes of video data in parallel.
• Developer SDKs and APIs: Power the NomadicML Python SDK used for programmatic video ingestion, analysis, and search - the core tool researchers and customers rely on.
• End-to-End Observability: Build logging, tracing, and metrics pipelines that surface GPU utilization, job latency, and per-video inference health.
• Lightweight Frontend Integrations: Support the web app’s Cloud Integrations and Project Workflows through backend endpoints and TypeScript SDK bindings.

You Might Be a Fit If You Have

• Deep proficiency in Python, Go, or TypeScript for backend systems.
• Experience with AWS, GCP, or Azure (IAM, S3/Blob Storage, Batch/Compute APIs, etc.).
• Strong understanding of GPU inference scaling, Kubernetes, container orchestration, and event-driven pipelines.
• Prior experience designing REST/gRPC APIs, SDKs, or developer-facing infrastructure.
• Familiarity with asynchronous job orchestration (Ray, Airflow, Dagster, Temporal).
• A practical mindset: you take research-grade systems and make them reliable, fast, and usable.

Nice to Have

• Experience contributing to inference orchestration frameworks or ML infra tools (e.g., DeepSpeed, Triton, Ray Serve).
• Understanding of video encoding, chunking, and streaming formats for efficient multi-modal ingestion.
• Basic front-end experience (React / Next.js) for integrating backend pipelines into product workflows.
• Background in ML infrastructure, observability, or data management systems.