Staff ML Systems & Reliability Engineer

Servicenow; Mountain View (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

ServiceNow is seeking a hands-on Staff Engineer to move ML models, agentic workflows, and self-learning approaches from prototypes into secure, observable production systems. You’ll partner with ML, data, product, and infrastructure teams to create a paved path from experimentation to production.

This role sits at the intersection of ML systems, platform engineering, and site reliability engineering. You will own the performance and evolution of systems deployed at scale.

Qualifications

• 7+ years of experience in software engineering, platform engineering, SRE, production engineering, or ML infrastructure.
• Strong software-engineering skills in Python and at least one production systems language such as Go, Java, C++, or Rust.
• Experience designing, operating, and troubleshooting distributed production systems, including failure analysis, capacity planning, and performance optimization.
• Hands-on experience with cloud infrastructure, containers and Kubernetes, infrastructure as code, CI/CD, and modern observability.
• Practical understanding of the ML lifecycle—including training, evaluation, model deployment, serving, monitoring, versioning, and retraining.
• Experience distinguishing service-health problems from data-quality or model-quality problems.
• Familiarity with SRE practices such as SLIs/SLOs, error budgets, sustainable on-call, incident management, and blameless postmortems.
• A strong automation and internal-customer mindset: you build platforms that are reliable, understandable, and pleasant for other engineers to use.
• Excellent technical judgment and communication skills, especially when navigating ambiguity and coordinating across teams during production incidents.

Responsibilities

• Design and build the ML production path from data prep to retraining.
• Create continuous-delivery workflows for models and agent workflows.
• Implement safe rollout patterns like canaries and feature flags.
• Define SLIs, SLOs, alerts, and error budgets across infra and data.
• Improve scalability, latency, and cost of distributed training and inference.

Skills

Python

Go

Java

C++

Rust

Distributed systems

Kubernetes

CI/CD

Observability

ML infrastructure

Tools

Kubernetes

CI/CD

Job description

ServiceNow is seeking a hands-on Staff Engineer to move ML models, agentic workflows, and self-learning approaches from prototypes into secure, observable production systems. You’ll partner with ML, data, product, and infrastructure teams to create a paved path from experimentation to production.

This role sits at the intersection of ML systems, platform engineering, and site reliability engineering. You will own the performance and evolution of systems deployed at scale.