Member of Technical Staff, Backend

About NomadicML

Americans drive over 5 trillion miles a year more than 500 billion of them recorded. Buried in that footage is the next frontier of machine intelligence. At NomadicML were building the platform that unlocks it.

Our Vision-Language Models (VLMs) act as the new hydraulic mining for video transforming raw footage into structured intelligence that powers real-world autonomy and robotics. We partner with industry leaders across self-driving robotics and industrial automation to mine insights from petabytes of data that were once unusable.

NomadicML was founded by Mustafa Bal and Varun Krishnan who met at Harvard University while studying Computer Science.

• Mustafa is a core contributor to ONNX Runtime and DeepSpeed with deep expertise in distributed systems and large-scale model training infrastructure

• Varun is an INFORMS Wagner Prize Finalist for his research in large-scale driver navigation AI models and one of the top chess players in the US.

Our team has built mission-critical AI systems at Snowflake Lyft Microsoft Amazon and IBM Research holds top-tier publications in VLMS and AI at conferences like CVPR and moves with the speed and clarity of a startup obsessed with impact.

About the Role

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

Youll build and scale the backbone that powers NomadicMLs video intelligence platform from secure cloud ingestion to distributed GPU inference pipelines that run our largest foundation models. Youll 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. Youll be working across cloud storage inference scheduling GPU clusters and the NomadicML SDK.

What Youll 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 apps 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 / ) for integrating backend pipelines into product workflows.

• Background in ML infrastructure observability or data management systems.