Machine Learning Engineer - Large Behavior Models - Computer Vision

Machine Learning Engineer – Large Behavior Models – Computer Vision

4+ years of professional ML engineering experience at an AI/ML-focused organization.
Familiarity with the state-of-the-art in behavior learning, language, and/or computer vision.
Experience training large-scale foundation models (VLMs, text-to-video models, etc) utilizing distributed training and high-performance optimization techniques such as quantization, mixed precision, model parallelism, data parallelism or FSDP.
Extensive practical experience with PyTorch.
Strong proficiency in Python and software development best practices such as unit testing, documentation, code review, continuous integration, and dependency management.
Familiarity with data pipelines, model serving and optimization, cloud training, and dataset management.
An ability to move fast and switch between modes of rapid prototyping and robust implementation as required.

Collaborate with internal research scientists and our partner labs at top academic research universities, including MIT, Stanford, Berkeley, CMU, Columbia, and Princeton, to drive pioneering research at scale.
Build, improve, and robustify end-to-end integrated ML pipelines for training multimodal (language, images, video, actions) models at scale.
Train, finetune, and serve robot foundation models with a strong MLOps mindset.
Build processes for integrating collaboration-produced and open-source advancements and code into our internal stack.
Build and improve large data pipelines for foundation model training.
Be a key member of the team and play a critical role in rapid progress measured by both the development of internal capabilities and high-impact external publications.