Staff Machine Learning Engineer

Staff 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 Staff Machine Learning Engineer to raise the bar for AI system design and ML engineering across our B2B SaaS platform. This is a senior IC role focused on building production-grade AI systems where ambiguity, context constraints, scalability, and operational correctness are first-order concerns. You will lead complex, cross-team initiatives while remaining hands-on in designing, building, and operating AI/ML systems. Responsibilities - Lead the design and development of AI/ML systems starting from problem framing, decision boundaries, and operational constraints. - Drive system and architecture decisions for AI and ML platforms, ensuring scalability, performance, and operational excellence. Design and evolve context engineering strategies for LLM-based systems under strict context window and latency constraints. - Architect agent-based systems, including agent roles, interaction models, coordination strategies, and failure handling. - Build and operate production AI systems with ownership of reliability, latency, cost, and correctness. - Define evaluation frameworks for AI and agent behavior beyond offline model metrics. - Scale AI-assisted development and reusable AI workflows to improve engineering velocity and system quality. - Review and approve ML designs, guiding tradeoffs across model quality, system complexity, and business impact. Mentor senior engineers and data scientists, raising standards in AI system design, ML engineering, and production readiness. - Partner with product and engineering leaders to translate ambiguous business problems into well-scoped, high-impact AI/ML solutions. - Contribute to multi-quarter technical strategy, aligning AI and ML investments with company objectives. Requirements - 12+ years of experience across AI, ML, and software engineering roles. - Strong coding skills in Python, Scala, or Java, with a track record of production-quality systems. - Hands-on experience in building, evaluating, and operating LLM-powered systems in production. - Strong understanding of context engineering, including information prioritization, relevance, and degradation under constraints. - Experience designing AI workflows or agentic systems, including task decomposition, orchestration, and failure handling. - Deep hands-on expertise in ML system design, NLP, feature engineering, and operational ML. - Experience deploying AI/ML systems on at least one major cloud platform. Hands-on experience with Big Data or streaming systems (e.g., Spark, Kafka, Airflow). - Proven ability to lead through influence, mentor engineers, and drive cross-team initiatives. - Strong business and product mindset, with the ability to map AI/ML solutions to measurable outcomes. Level Expectations - Delivers multi-quarter AI/ML initiatives impacting multiple teams and products. - Acts as a go-to expert for AI system design including context engineering, agent architectures, NLP, and ML system design. - Raises the overall AI maturity of the organization through architecture, m ... (truncated, view full listing at source)