Staff Applied AI Engineer

We’re on a mission to democratize AI by building the definitive AI data development platform. The AI landscape has gone through incredible change between 2016, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

As an Applied AI Engineer, you’ll research and utilize established and state-of-the-art Gen AI and machine learning (ML) techniques to successfully deliver solutions to our customers. You’ll work directly with our customers to understand their business and technical needs. You will design and deliver AI solutions, either by leveraging Snorkel Flow or developing custom approaches when needed, and collaborate with product and engineering teams to help generalize and integrate these learnings back into the Snorkel Flow platform. We move fast and are constantly prototyping new ways to deliver value to our customers. This position is ideal for someone who enjoys solving complex problems, working directly with customers, and bridging the gap between AI technology and business value.

Main Responsibilities

• Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. This may involve leveraging Snorkel Flow or designing custom approaches using state-of-the-art tools, with the goal of delivering real business value and informing the evolution of the Snorkel platform.
• Develop and implement state of the art AI techniques such as retrieval-augmented generation (RAG), fine-tuning, prompt engineering, AI agents, ML model training, and optimizing model performance for real-world deployment to maximize business impact.
• Forge and manage relationships with our customers’ leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow.
• Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI.
• Serve as the voice of our customers for new AI paradigms, data science workflows, and share customer feedback to product teams
• Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI.
• Annual travel up to 25%.

Preferred Qualifications

• 5+ years of customer-facing experience in the design and implementation of AI/ML solutions.
• Proficient in Python.
• Expertise in AI frameworks and libraries, such as scikit-learn, PyTorch, Hugging Face Transformers, LangChain, and OpenAI APIs.
• Experience leading strategic, customer-facing initiatives and collaborating with business stakeholders to ensure ML solutions drive successful business outcomes, with a strong focus on teaching and enablement.
• Outstanding presentation skills to technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos.
• Ability to work in a fast-paced environment and balance priorities across multiple projects at once.
• B.S. degree in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics, or comparable degree/experience.

Compensation range for Tier 1 locations of San Francisco, Ca or Redwood City, CA  $210K - $275K OTE. All offers also include equity in the form of employee stock options. Our compensation ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Locations

San Francisco, CA (Hybrid) or Redwood City, CA (Hybrid)

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