Member of Technical Staff, Machine Learning - NomadicML

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, we’re 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 byMustafa Bal andVarun Krishnan, who met atHarvard University while studying Computer Science.

• Mustafa is a core contributor toONNX Runtime andDeepSpeed 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 atSnowflake, Lyft, Microsoft, Amazon, and IBM Research, holds top-tier publications in VLMS and AI at conferences likeCVPR, and moves with the speed and clarity of a startup obsessed with impact.

About the Role

We’re seeking aMachine Learning Engineer who thrives at the frontier offoundation-model research and production engineering.
You’ll help define how machines learn from motion: training and fine-tuning large-scaleVision-Language Models to reason about complex, real-world video.

Your work will involve building multi-modal architectures that perceive, localize, and describe motion events (turns, lane changes, interactions, anomalies) across millions of frames, and turning those breakthroughs into robust APIs and SDKs used by enterprise customers.

You’ll work directly with the founders to:

• Train and evaluateVLMs specialized for motion understanding in autonomous-driving and robotics datasets.

• Design and scaleGPU-accelerated pipelines for training, fine-tuning, and inference on multi-modal data (video + language + sensor metadata).

• Buildagentic evaluation frameworks that benchmark spatiotemporal reasoning, localization accuracy, and narrative consistency.

• Develop and productionizecuration loops that use our own models to generate and refine datasets (“AI training AI”).

• Publish high-impact research (e.g., NeurIPS, CVPR) while shipping features that customers use immediately.
  

You’ll Excel If You Have

• Strong proficiency inPython,PyTorch, and large-scale ML workflows.

• Research experience infoundation models, VLMs, or multi-modal learning (publications/patents a plus).

• Ability to iteratequickly and autonomously, running experiments end-to-end.

• Experience training or fine-tuning models onvideo or sensor data.

• Understanding ofretrieval systems, embeddings, and GPU optimization.

Nice to Have

• Contributions to open-source ML frameworks (e.g., DeepSpeed, Hugging Face).

• Experience withvector databases,distributed training, orML orchestration systems (e.g., Ray, Kubeflow, MLflow).

• Prior exposure toautonomous-driving or robotics datasets.