Machine Learning Engineer
Confirmed on the employer's own hiring board on Oct 8, 2026. First seen by Alion on Oct 8, 2026.
Join ServiceNow as a Machine Learning Engineer, where you will design, build, deploy, and operate services that enhance the platform's multimodal AI capabilities. You will work on document extraction, visual understanding, and agentic automation, integrating LLMs into production systems. Your responsibilities will include ensuring the quality and reliability of the code, deploying and operating on Kubernetes, building product features end to end, and collaborating across teams. This role requires a strong background in machine learning, software engineering, and experience with Docker and Kubernetes.
Missions
- Conception, construction, deployment, and operation of services related to multimodal AI capabilities.
- Ownership of the quality and correctness of the code, whether written by a human or AI coding agents.
- Collaboration with product managers, engineers, designers, and other teams to define success criteria and communicate capabilities.
Profil recherché
- This role needs someone who cares deeply about building production-ready ML services: designing clean APIs and pipelines, deploying and scaling services on Kubernetes, and keeping them fast, observable, and resilient- Growth mindset. Eagerness to learn, take ownership, and grow in a collaborative team
- Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry
- AI-native approach. Curiosity and a track record of using AI tools to improve engineering workflows
- Production mindset. Experience building, deploying, and operating services, with attention to scalability, observability, and reliability
- Hands-on experience with Docker and Kubernetes
- Hands-on multimodal experience. Projects, research, or work involving document understanding or multimodal models is a strong plus
- Software engineering fundamentals. Strong command of data structures, algorithms, system design, APIs, concurrency, and testing. Java or JavaScript experience is a bonus
- Computer vision and model evaluation. Understanding computer vision techniques and the ability to evaluate model quality independently, including designing test sets and choosing the right metrics
- ML foundations. Solid understanding of machine learning fundamentals and how LLMs and vision-language models are integrated into applications
- Master's degree in Computer Science, Machine Learning, or a related technical field, with 1 to 3 years of related experience