1 year ago
Responsibilities
- Train and evaluate Vision-Language Models for motion understanding in autonomous-driving and robotics datasets.
- Design and scale GPU-accelerated pipelines for multimodal training, fine-tuning, and inference across video, language, and sensor metadata.
- Build agentic evaluation frameworks for spatiotemporal reasoning, localization accuracy, and narrative consistency.
- Develop and productionize model-driven data curation loops that generate and refine datasets.
- Turn research advances into robust APIs and SDKs used by enterprise customers.
- Publish high-impact research while shipping customer-facing product features.
Requirements
- Strong proficiency in Python, PyTorch, and large-scale machine learning workflows.
- Research experience with foundation models, Vision-Language Models, or multimodal learning.
- Ability to run experiments end-to-end and iterate quickly and autonomously.
- Experience training or fine-tuning models on video or sensor data.
- Understanding of retrieval systems, embeddings, and GPU optimization.
- Contributions to open-source ML frameworks, experience with vector databases, distributed training, or ML orchestration systems are preferred.
- Prior exposure to autonomous-driving or robotics datasets is preferred.
Categories
About Pear VC
Pear VC is an early-stage venture capital partnership that invests at pre-seed and seed, pairing capital with hands-on support, mentorship, and programs like its PearX accelerator for founding teams. Founded in 2013 and headquartered in Menlo Park, California, it backs software and technology startups across categories. Its portfolio includes companies that reached the public markets, such as DoorDash and Guardant Health.