Software Engineer, Snorkel AI

• Bachelor’s degree in Computer Science, Machine Learning, or a related field
• 1-2 years of experience in AI development or backend engineering
• Strong coding skills in Python and familiarity with deep learning frameworks like PyTorch or TensorFlow
• Passion for AI development and willingness to learn advanced techniques
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field
• 4-6 years of experience in A and or backend development, including hands-on work with AI pipelines in production systems
• Strong expertise in Python and deep learning frameworks such as PyTorch
• Proven ability to lead projects and mentor team members
• Solid understanding of LLM architectures, fine-tuning, and deployment
• Solid understanding of agentic workflows and tooling (Langchain, Crew.ai etc)
• Experience building scalable CI/CD pipelines for machine learning workflows

Desirable

• As an AI Platform Engineer, you will be at the forefront of designing and implementing the core AI pipelines that drive SnorkelFlow’s AI capabilities
• Whether you’re an entry-level engineer eager to grow in the field or a senior engineer ready to lead high-impact projects, this role offers opportunities to work on SOTA AI models and redefine the AI stack through a data-driven primary way to solve challenging problems
• Contribute to designing and maintaining AI pipelines for labeling, embeddings, training, and deploying models into production
• Collaborate with the Compute Platform team to ensure seamless integration with orchestration tools and infrastructure
• Define and develop AI Platform APIs for other applications to use them
• Build and deploy LLM-based systems for production workflows, focusing on efficiency, scalability, and reproducibility
• Implement training pipelines for LLMs, including fine-tuning, pruning, distillation, and foundational model training
• Integrate APIs from providers such as OpenAI, Anthropic, and Gemini into SnorkelFlow’s pipelines
• Develop and integrate backend services to manage LLM calls and API interactions
• Work with the Data Platform team to define data requirements and ensure smooth interoperability
• Partner with the Application team to design and implement APIs that power workflows
• Implement tools and dashboards to monitor and track AI pipeline performance
• Define metrics to ensure system health and support optimization efforts
• Innovate on advanced training methodologies for LLMs and NLP systems, including state-of-the-art optimization techniques
• Take ownership of end-to-end model lifecycle management, from training to production deployment