Data Scientist, Principal - AI Product Engineering
Blue Shield of California26 days ago
Oakland, CA, USAStaff+
Responsibilities
- Lead the development and deployment of generative AI applications and set technical direction for AI and machine learning across the organization.
- Design and build scalable LLM applications, copilots, agents, RAG and search capabilities, and AI-enabled automation embedded in products and enterprise workflows.
- Prototype, evaluate, and iterate on new AI features and lead architecture and development of products from 0 to 1.
- Build APIs, backend services, data pipelines, and retrieval pipelines that expose AI capabilities to applications.
- Collaborate with researchers and product managers to translate AI research into tangible product features.
- Optimize software performance and reliability of deployed applications and champion best practices for generative AI development and deployment.
- Evaluate model performance, implement improvements, and support responsible and compliant AI.
- Mentor and develop team members while fostering a collaborative, high-performing environment.
Requirements
- Bachelor’s degree in computer science or a quantitative discipline, or equivalent practical experience; a master’s degree or PhD is preferred.
- At least 10 years of relevant experience in data science, machine learning, applied AI/ML, software engineering, or advanced analytics.
- Proven experience rapidly building and shipping software products, rather than only developing models or analyses.
- Strong software engineering skills and proficiency in Python, including API and backend-service development.
- Experience leading ML design and optimizing ML infrastructure, model deployment, evaluation, and data processing.
- Hands-on experience with deep learning and LLM application frameworks including PyTorch, TensorFlow, LangChain, and LangGraph.
- Hands-on experience building generative AI applications, including prompt engineering and retrieval-augmented generation.
- Preferred experience with generative AI research or applications and agent-based systems and orchestration frameworks.
- Preferred experience with Azure, Google Cloud, or AWS and scalable data processing with SQL or Spark.
- Preferred MLOps and LLMOps experience, including CI/CD, monitoring, and model lifecycle management.
- Preferred experience shipping software in fast-paced, customer-facing environments and understanding responsible AI and governance in regulated or healthcare settings.
Benefits
- Hybrid workplace model with flexibility and purposeful in-person collaboration; most teams work in the office two days per week.
- Employees located more than 50 miles from an office determine in-office time with their manager based on business need.
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