Senior Machine Learning Engineer

Senior Machine Learning Engineer Introduction to Demandbase: Demandbase is the only pipeline AI platform that empowers GTM teams to automate growth at scale. With a unified view of data, insights, actions, and outcomes, B2B enterprises can seamlessly align and execute their account-based GTM strategies with confidence. Thousands of businesses trust Demandbase to maximize revenue, minimize waste, and consolidate their data and tech stacks – all in one platform. As a company, we’re as committed to growing careers as we are to building world-class technology. We invest heavily in people, our culture, and the community around us. We have also continuously been recognized as One of The Best Places To Work in the San Francisco Bay Area by Fortune, and One of The 60 Best Companies To Sell For by Selling Power. Our offices are located in San Francisco, New York, Austin, Seattle, India, and the United Kingdom. About the Role We are looking for a Senior Machine Learning Engineer to help architect and build next-generation Agentic AI systems at Demandbase. This role focuses on multi-agent orchestration, LLM-powered reasoning systems, evaluation frameworks, guardrails, and scalable GenAI architectures. You will work at the intersection of advanced data science, generative AI research, and production-grade ML systems, shaping how intelligent agents operate reliably, safely, and effectively in enterprise environments. This is not a platform infrastructure role — it is a deep AI systems engineering role centered around agent architecture, model evaluation, reasoning systems, and applied ML innovation. Key Responsibilities Agentic AI & Multi-Agent Architecture - Design and implement multi-agent systems for complex enterprise workflows. - Build agent orchestration frameworks (planner-executor, tool-using agents, retrieval-augmented agents, self-reflective agents). - Develop architectures for reasoning loops, memory systems, tool integration, and contextual grounding. - Design guardrails for hallucination mitigation, tool misuse prevention, and safe execution. - Implement feedback-driven refinement loops and self-correction strategies. GenAI Systems, Evals & Guardrails - Design and operationalize LLM evaluation frameworks (automated evals, LLM-as-judge, human-in-the-loop, adversarial testing). - Build robust prompt engineering and prompt versioning strategies. - Develop safety guardrails including content filtering, policy enforcement, and bias monitoring. - Implement quality metrics for: - Factual accuracy - Groundedness - Latency and cost efficiency - Agent reliability - Create structured evaluation pipelines to continuously improve agent performance. Advanced Data Science & NLP - Apply advanced NLP techniques (transformers, embeddings, fine-tuning, RAG pipelines). - Work deeply with unstructured and semi-structured data. - Develop model experimentation frameworks for prompt optimization, fine-tuning, and retrieval strategies. - Optimize data pipelines using Python, Pandas, Spark, and vector databases. - Collaborate with data scientists to convert research prototypes into scalable AI systems. AI System Design & Architecture - Architect modular, extensible AI systems for long-term maintainability. - Design retrieval-augmented generation (RAG) systems with advanced chunking, embedding strategies, and re-ranking. - Build memory architectures (short-term, long-term, vector-based). - Optimize inference pipelines for performance, cost, and reliability. - Define reusable patterns for enterprise-grade AI systems. Operational Excellence for AI Systems - Implement evaluation-driven CI/CD for GenAI systems. - Establish monitoring for: - Model drift - Agent failure modes - Tool misuse - Hallucination frequency - Maintain reproducibility through experiment tracking and versioning. - Ensure ethical AI practices and compliance with enterprise standards. Technical Leadership & Mentorship - Define best practices ... (truncated, view full listing at source)