Engineering Internship, Enrichment and Curation
$207,501–$207,501 year
Hybrid · Sunnyvale, California, United States
Job Summary
Engineering Internship to propel embodied foundation models research. Responsibilities include working on foundation models for embodied AI (pretraining and post-training), training on large-scale multimodal data in a multi-node distributed system, curating large multimodal datasets for training and evaluation, and leading high-impact research work with potential publications at top conferences (e.g., CVPR, ICCV, CoRL, NeurIPS, CoLM, RSS, ICRA). Requires experience with vision-language models, large language models, NLP focusing on reasoning, data curation, solid Python, and PyTorch (or TensorFlow/JAX). The role is full-time, hybrid in Sunnyvale, CA, with an hourly rate around $99.76/hour (~$207,501/year). Emphasis on pursuing a graduate degree in CS/ML/Robotics and openness to self-driving/embodied AI research. Wayve promotes inclusive culture; US-based applicant information and accommodations are noted.
Required Qualifications
- Currently pursuing a graduate degree in Computer Science, Machine Learning, Robotics, or related technical field
- Proficient in at least one backend/systems programming language (e.g. Python, Ruby, Java, etc.)
- Experience with vision-language models, large language models, NLP, especially around reasoning
- Experience curating training data to steer model behavior
- Solid software engineering fundamentals, especially in Python
- Previous use of PyTorch or similar library for deep learning (TensorFlow, JAX)
- Experience with multi-node distributed training of large models
- Interested in using large-scale multimodal datasets to improve embodied AI
- Publications in conferences such as CVPR, ICCV, CoRL, NeurIPS, CoLM, RSS, ICRA
Desired Qualifications
- Experience with vision-language models, large language models, NLP focused on reasoning
- Experience curating training data to steer model behavior
- Solid software engineering fundamentals in Python
- Experience with PyTorch or similar (TensorFlow, JAX)
- Experience with multi-node distributed training of large models
- Interest in large-scale multimodal datasets for embodied AI
- Publications in CVPR, ICCV, CoRL, NeurIPS, CoLM, RSS, ICRA (or similar)
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