Principal Applied Science Engineer

Do you want to shape the future of the autonomous enterprise and lead the development of intelligent, agent-first experiences that transform how businesses operate? The Business and Industry Solutions (BIS) team is looking for a Senior Applied Scientist to drive innovation at the intersection of AI, experimentation, and enterprise systems. In this role, you will design and evaluate autonomous agents that deliver measurable improvements in accuracy, latency, and cost-efficiency. You’ll lead rapid experimentation cycles, develop robust evaluation frameworks, and apply advanced techniques like reinforcement learning to enable multi-step reasoning and decision-making. You’ll collaborate across engineering, product, and partner teams to ensure agents are performant, secure, reliable, and extensible—empowering customers and partners to build on our platform. This is your opportunity to influence the next generation of AI-native business applications and deliver real-world impact at scale.   

The ideal candidate has prior expertise in natural language processing (NLP), with a strong foundation in large language model (LLM) development, evaluation, and fine-tuning. They should have hands-on experience in applying advanced fine-tuning techniques—including instruction tuning, reinforcement learning from human feedback (RLHF), and tool-augmented generation—to build agents capable of multi-step reasoning and decision-making. Familiarity with prompt/context engineering, context-aware orchestration, and integrating LLMs with external tools and APIs is essential. The candidate should be comfortable working in a fast-paced, experimentation-driven environment, leveraging both offline and online evaluation methods to iterate rapidly and optimize agent behavior. A deep understanding of the challenges and opportunities in building AI-native enterprise applications will be key to success in this role.  

• Drive strategic impact by identifying and leading high-leverage data science and analytics initiatives across multiple product domains.  
• Lead the development and deployment of advanced model fine-tuning pipelines, leveraging Reinforcement Learning from Human Feedback (RLHF) to align AI system behavior with human intent and improve performance in complex, real-world enterprise scenarios.  
• Steer strategic direction and investment decisions by owning complex, end-to-end projects that blend technical depth with organizational influence.  
• Build alignment and trust across leadership and cross-functional teams through clear, persuasive communication and collaborative engagement.  
• Design and implement robust measurement systems, experimentation frameworks, and causal inference methodologies tailored to dynamic AI systems and enterprise-scale environments.  
• Mentor and elevate the data science community by championing best practices, nurturing talent, and cultivating a collaborative, high-performance culture.  
• Harness AI to accelerate workflows and amplify team productivity through intelligent automation and innovation.