Phaselaw
Member of Technical Staff, Machine Learning - NomadicML
San Francisco · Staff+
Sponsorship not specifiedDetected 266 days ago
PythonDistributed SystemsSnowflakeVector DatabasesMachine LearningPyTorchExcelRoboticsResearch
About the role
- We're seeking a Machine Learning Engineer who thrives at the frontier of foundation-model research and production engineering.
- You'll help define how machines learn from motion: training and fine-tuning large-scale Vision-Language Models to reason about complex, real-world video.
- You'll work directly with the founders to:
Responsibilities
- Design and scale GPU-accelerated pipelines for training, fine-tuning, and inference on multi-modal data (video + language + sensor metadata).
- Develop and productionize curation loops that use our own models to generate and refine datasets ("AI training AI").
Nice to have
- Contributions to open-source ML frameworks (e.g., DeepSpeed, Hugging Face).
- Experience with vector databases, distributed training, or ML orchestration systems (e.g., Ray, Kubeflow, MLflow).
- Prior exposure to autonomous-driving or robotics datasets.
- Train and evaluate VLMs specialized for motion understanding in autonomous-driving and robotics datasets.
- Strong proficiency in Python, PyTorch, and large-scale ML workflows.
- Ability to iterate quickly and autonomously, running experiments end-to-end.
- Experience training or fine-tuning models on video or sensor data.
- Understanding of retrieval systems, embeddings, and GPU optimization.
Benefits
- training and fine-tuning large-scale Vision-Language Models to reason about complex, real-world video.
Company info
- Publish high-impact research (e.g., NeurIPS, CVPR) while shipping features that customers use immediately.
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This listing is sourced directly from Phaselaw's careers page and normalized into a canonical job model.