About the job
About Demandbase:
Demandbase stands as the sole pipeline AI platform tailored for GTM teams seeking to automate expansive growth. By offering a consolidated perspective of data, insights, actions, and outcomes, B2B enterprises can confidently synchronize and execute their account-based GTM strategies. Our platform is trusted by thousands of businesses aiming to enhance revenue, reduce waste, and streamline their data and technology stacks.
We are deeply committed to nurturing careers while also developing cutting-edge technology. Our investment in our people, culture, and community is paramount. Demandbase has consistently been acknowledged 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. We have offices in San Francisco, New York, Austin, Seattle, India, and the United Kingdom.
Role Overview
We are in search of a Senior Staff Machine Learning Engineer to elevate the standards of AI system design and ML engineering within our B2B SaaS platform. This senior individual contributor role is centered on developing production-grade AI systems where factors like ambiguity, contextual constraints, scalability, and operational correctness are paramount. You will spearhead intricate, cross-team initiatives while remaining actively involved in the design, construction, and operation of AI/ML systems.
Main Responsibilities
Direct the design and development of AI/ML systems from problem framing to operational constraints.
Steer system and architecture decisions for AI and ML platforms, ensuring scalability, performance, and operational excellence, along with evolving context engineering strategies for LLM-based systems under strict constraints.
Design agent-based systems, including roles, interaction models, coordination strategies, and failure handling.
Construct and maintain production AI systems, taking ownership of reliability, latency, cost, and correctness.
Establish evaluation frameworks for AI and agent behaviors beyond offline metrics.
Enhance AI-assisted development and reusable workflows to boost engineering velocity and system quality.
Review and endorse ML designs, guiding trade-offs related to model quality, system complexity, and business impact while mentoring senior engineers and data scientists to elevate standards in AI system design and ML engineering.

