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Demandbase is a pipeline AI platform that empowers go-to-market teams to automate growth at scale. The company provides a unified view of data, insights, actions, and outcomes for B2B enterprises to execute account-based GTM strategies. Demandbase is recognized as one of the best places to work in the San Francisco Bay Area and operates offices globally including in San Francisco, New York, Austin, Seattle, India, and the United Kingdom.
The Machine Learning Engineer II role is a hands-on position focused on building intelligent data and AI/ML capabilities for the Company and Domains Data teams. This is a full-stack ML engineering role requiring strong ML fundamentals, GenAI/LLM expertise, data engineering skills, and production software engineering capabilities.
Key responsibilities include designing and productionizing machine learning and GenAI solutions for company and domain intelligence. You will build data pipelines using LLMs, transformers, and RAG techniques to solve classification, enrichment, information extraction, ranking, and data quality problems. The role involves experimenting with models, prompts, embeddings, and retrieval techniques while collaborating with engineers, analysts, and product managers to translate requirements into scalable solutions.
You will develop reusable AI services, APIs, and orchestration components, and build LLM applications using RAG, semantic retrieval, tool/function calling, and agentic workflows. The position requires building scalable data and feature pipelines to process large volumes of structured, semi-structured, and unstructured first-party and third-party data. You will develop evaluation datasets, automated evaluation frameworks, and feedback loops for AI/ML features, defining quality metrics for accuracy, relevance, grounding, latency, reliability, and cost.
Production excellence is critical: you will build clean, scalable, maintainable production-grade AI/ML systems, own features end-to-end from design through deployment and monitoring, and operate cloud-native services using Docker, Kubernetes, and CI/CD. The role requires strong Python programming, SQL expertise, and experience with cloud platforms (AWS, GCP, or Azure). Experience with Spark, Kafka, Airflow, entity resolution, Kubernetes, and MLOps/LLMOps is valued.
