Staff Machine Learning Engineer, Agentic App Platform

ServiceNow is expanding its enterprise-grade generative AI conversation platform and seeking a Staff Machine Learning Engineer to join the Agentic App Platform team in Mountain View, CA on site. The role concentrates on scaling and optimizing a cutting-edge, enterprise-ready conversational engine, building scalable product infrastructure and APIs in collaboration with ML, application, and product teams.

Responsibilities

• Build and scale a broad product infrastructure with intuitive interfaces that let engineers and configurators tailor and optimize generative AI models, while gathering data and feedback to adapt to different enterprise contexts.
• Deliver domain-specific conversational experiences tailored to diverse enterprise use cases.
• Create scalable API abstractions for the conversation platform that support major chat clients including MSTeams, Slack, and Web, while exposing a neutral API for other parts of the engineering system.
• Optimize the dialog engine to support a wide array of features, leveraging private domain knowledge in the cloud and enabling real-time multilingual translation, all with a minimal memory footprint and low latency to streamline development for engineers.
• Promote best practices in coding patterns, API design, scalability, robustness, and performance optimization, fostering a culture of excellence and continuous improvement among engineers.
• Provide comprehensive visibility into product performance through robust logging and tracing, intuitive debugging and triage tooling, and automated metrics for monitoring and analysis.
• Collaborate closely with ML engineers, application engineers, product teams, and customer support to drive feature development and scalability initiatives.

Requirements

• Strong foundation in computer science and software engineering with experience building scalable systems.
• Deep understanding of clean, modular, and scalable API design with the ability to champion coding standards and elevate code quality among engineers.
• Passion for optimizing systems, proficient with tracing, logging, and metrics frameworks, and a methodical approach to identifying and resolving latency bottlenecks and throughput constraints.
• Ability to independently research new requirements and craft innovative solutions, thriving in a fast-paced, iterative development environment.
• Excellent communication skills and a talent for cross-functional collaboration, with the ability to articulate design rationales clearly.
• Bachelor’s degree or higher in computer science or a related field.
• 7+ years of professional development experience, specifically building systems at scale.

Technologies

• MSTeams
• Slack
• Web

The role

The role centers on scaling and refining a cutting-edge Generative AI product that delivers instant enterprise assistance. It invites you to explore how abstraction, scalability, and optimization apply to a dynamic, probabilistic, generative conversational system. You will join the Conversation Engine team to contribute at the core of the product.

What you get to do in this role

• Build an extensive product infrastructure with intuitive interfaces that empower engineers and configurators to customize and optimize generative AI models, while collecting data and feedback to support diverse enterprise use cases and domain-specific conversations.
• Design scalable API abstractions for the conversation platform that works across major chat clients (MSTeams, Slack, Web) and provides a neutral API for other parts of the engineering system.
• Optimize the dialog engine to support a broad set of features, leveraging private enterprise knowledge in the cloud and enabling real-time multilingual translation, with a small memory footprint and low latency to streamline development.
• Champion best practices in coding patterns, API design, scalability, robustness, and optimization, fostering a culture of excellence and continuous improvement among engineers.
• Provide visibility into product performance via a robust logging and tracing framework, intuitive debugging and triaging tools, and automated metrics for efficient monitoring and analysis.
• Collaborate closely with ML engineers, application engineers, product teams, and customer support to drive feature development and scalability initiatives.