ML Systems Integration Engineer

Job Title

ML Systems Integration Engineer

Role Summary

The ML Systems Integration Engineer will support bring-up, validation, and debugging of next-generation AI hardware systems and their supporting software. The role focuses on reproducing and diagnosing system-level failures, building automation and tooling to improve validation workflows, and collaborating with hardware and firmware teams to drive systems toward production readiness.

Experience Level

Mid-level. No explicit years of experience specified.

Responsibilities

Primary responsibilities include system bring-up, debugging, and automation to accelerate hardware validation and deployment.

• Participate in bring-up of new AI hardware systems and associated software infrastructure.
• Reproduce, triage, and diagnose complex system-level issues spanning hardware and software.
• Investigate failures using logs, telemetry, and diagnostic tools to identify root causes.
• Develop software and test frameworks to validate and stress distributed hardware systems.
• Build automation and internal tooling to improve validation, observability, and debugging workflows.
• Collaborate closely with hardware, firmware, and systems engineers to isolate integration issues.
• Support validation and qualification of new hardware generations toward production readiness.
• Continuously improve engineering workflows related to debugging, testing, and automation.

Requirements

Must-have technical skills and behaviors for the role.

• Strong programming skills in Python and/or C++.
• Excellent debugging and systematic problem-solving ability for complex technical issues.
• Solid understanding of operating systems fundamentals: processes, threads, memory management, concurrency, and IPC.
• Experience working in Linux development environments.
• Understanding of computer architecture and hardware–software interactions.
• Ability to work effectively across multiple engineering teams and communicate technical issues clearly.

Nice-to-have:

• Experience building automation frameworks, internal tooling, or test infrastructure.
• Familiarity with distributed systems concepts and networking fundamentals.
• Experience debugging large-scale systems, performance analysis, system telemetry, or log analysis.
• Exposure to production systems validation or infrastructure reliability engineering.

Education Requirements

BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, as stated in the posting.

About the Company

Company: Cerebras

Headquarters: Sunnyvale, CA, USA

Developer of wafer-scale AI accelerators, Cerebras designs the Wafer Scale Engine (WSE)—one of the world’s largest AI chips—to deliver high-speed training and inference solutions for model labs, enterprises, and AI-native startups.