(This role can either be remote or a hybrid role based in Redwood City, Redwood City + San Francisco, or New York City.) 

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

• How to prompt a model like GPT-4 effectively?
• How to build a foundation model for a specific domain like medical records?
• How to use models like LayoutLM V3 for weakly labeling documents at scale?
• How to blend prompt engineering, retrieval augmentation, and fine-tuning to customize models with the least human time and effort?

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.  

Main Responsibilities

• 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)
• Work a hybrid schedule with one or two days per week in our Redwood City HQ and work remotely with "No Meeting" Tuesdays and Thursdays

Preferred Qualifications

• Broad expertise and understanding of AI, NLP, LLM, and generative AI trends.
• Experience prototyping, experimenting, and testing with large scale datasets and training deep models.
• 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.

The salary range for our Tier 1 locations of San Francisco, Seattle, Los Angeles & New York is $160,000 - $210,000. All offers include equity compensation in the form of employee stock options.

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