Staff Applied Research Scientist (Computer Vision)

As an Applied Research Scientist focused on Computer Vision, you will innovate and translate cutting edge research into user experiences. If you find yourself thinking about any of these questions:

• How to leverage LLM agents in conjunction with CV tools to solve CV problems at scale?
• How to build robust foundation models like CLIP with a rich semantic understanding but for detection and segmentation?
• How to solve multi-modal tasks like text-to-image retrieval or image classification in an open-vocabulary setting in expert domains where fine-grained semantic differences matter?
• How to use models like LayoutLM V3 for weakly labeling documents at scale?

We would love to hear from you! More broadly you will work on supervision (self-supervised, semi-supervised, weakly supervised etc) and representation learning methods which result in practical tooling for building and maintaining state-of-the-art machine learning models at scale.  

• Establish and empirically demonstrate the state-of-the-art approaches for data-centric model iteration and analysis
• Prototype end-to-end workflows with novel techniques and algorithms, synthesize results, and help to transfer learnings into Snorkel products
• Work closely with design partners to validate your work on real-world use cases with measurable impact
• Contribute to novel research on topics of interest to Snorkel AI by collaborating with other Snorkel Research scientists and affiliate scientists (academic, government, and industry researchers)

• Broad expertise and understanding of AI, CV, NLP, LLM, and generative AI trends.
• Experience with multi-modal foundation model research at the intersection of image and text
• Experience with training, using and adapting image generation models like stable-diffusion to specific domains
• Experience with model training and inference across a large compute cluster
• Experience with standard machine learning frameworks and tools (NumPy, Scikit-learn, Pandas, Pytorch, TensorFlow, etc.) and machine learning cloud infrastructure and accelerators (AWS, Google Cloud, GPUs and TPUs).
• Strong technical communication skills and the ability to work in a fast-paced environment.
• Experience with developing robust software with excellent coding hygiene and modular design.
• The position involves working with problems with no off-the-shelf solutions and requires innovation on-the-fly. Typically a Ph.D. in machine learning or a related area with good publication history would be a good fit for this position. We would also love to hear from people with similar skill sets acquired through other career paths.

We encourage applications from candidates who believe they satisfy many of the preferred qualifications, even if they do not satisfy all.