Member of Technical Staff, Machine Learning

Pear VC • Austin, TX

59d

About The Position

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. 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 evaluate VLMs specialized for motion understanding in autonomous-driving and robotics datasets. Design and scale GPU-accelerated pipelines for training, fine-tuning, and inference on multi-modal data (video + language + sensor metadata). Build agentic evaluation frameworks that benchmark spatiotemporal reasoning, localization accuracy, and narrative consistency. Develop and productionize curation 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.

Requirements

• Strong proficiency in Python, PyTorch, and large-scale ML workflows.
• Research experience in foundation models, VLMs, or multi-modal learning (publications/patents a plus).
• 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.

Nice To Haves

• 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.

Responsibilities

• Train and evaluate VLMs specialized for motion understanding in autonomous-driving and robotics datasets.
• Design and scale GPU-accelerated pipelines for training, fine-tuning, and inference on multi-modal data (video + language + sensor metadata).
• Build agentic evaluation frameworks that benchmark spatiotemporal reasoning, localization accuracy, and narrative consistency.
• Develop and productionize curation 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 11-50 employees