Staff AI Platform 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 a Staff AI Platform Engineer, you will play a pivotal role in designing and implementing the core AI pipelines that drive SnorkelFlow’s AI capabilities. You will act as a technical leader, setting architectural direction, mentoring team members, and collaborating across verticals to ensure the AI Platform aligns with product goals and organizational strategy.

This role requires hands-on experience with large language models (LLMs), requiring deep expertise in AI development, especially in designing and deploying systems that leverage LLMs and more traditional NLP solutions for production workflows.

Main Responsibilities

Technical Leadership

• Provide technical direction for the design and development of AI pipelines, ensuring scalability, robustness, and extensibility.
• Serve as a mentor and guide for engineers on the AI Platform team, fostering growth and technical excellence.
• Identify and drive high-impact projects aligned with business objectives and product needs.

AI Pipeline Development

• Architect, design, and maintain AI pipelines for labeling, embeddings, training, and deploying models into production.
• Lead the development and optimization of MLFlow pipelines for deployment.
• Build and deploy foundational models that serve as the backbone for SnorkelFlow’s core product capabilities.
• Partner with the Compute Platform team to ensure seamless integration with orchestration tools and infrastructure.

LLM Tooling development:

• Develop and deploy LLM-based systems for production workflows, focusing on efficiency, scalability, and reproducibility.
• Create AI training framework pipelines that will allow LLM usage in applications, including fine-tuning, pruning, distillation, and foundational model training.
• Integrate APIs from providers such as OpenAI, Anthropic, and Gemini into SnorkelFlow’s pipelines.

Backend Development

• Oversee the integration of backend services for managing LLM calls and API interactions.
• Collaborate with the Data Platform team to define data requirements and ensure smooth interoperability.
• Work with the Application team to design and implement APIs that power application workflows.

Observability and Monitoring

• Establish observability standards for AI pipelines, including tools and dashboards for monitoring model performance and debugging.
• Define key metrics for system health and optimization.

Cross-Functional Collaboration

• Act as a thought leader, collaborating with Data Platform, Compute Platform, Application, Product and other internal teams to deliver cohesive, scalable solutions.
• Partner with stakeholders to translate product goals into technical roadmaps and

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field.
• 8+ years of experience with a good portion of them in AI development, including hands-on work with AI in production systems.
• Strong expertise in Python and deep learning frameworks such as PyTorch.
• Proven experience leading technical projects and mentoring engineers.
• Proficiency with CI/CD pipelines for machine learning workflows.
• Deep understanding of LLM architectures, fine-tuning, and deployment methodologies.
• Strong communication skills, with an emphasis on scalable and reliable system design.

Preferred Qualifications

• Expertise in NLP and familiarity with libraries such as Hugging Face Transformers, spaCy, scikit-learn, or XGBoost.
• Familiarity with multimodal AI concepts, including vision and audio tasks.
• Experience working with APIs and foundational model providers such as OpenAI, Anthropic, or Gemini.
• Knowledge of MLOps tools and practices, such as MLflow, Kubernetes, or Ray.
• Experience building APIs or SDKs for AI services.

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