ML Systems Integration Engineer

Job Title

ML Systems Integration Engineer

Role Summary

Work on bring-up, validation, and integration of next-generation AI hardware systems and their supporting software. Collaborate with hardware and software engineering teams to diagnose system-level failures and improve tooling, observability, and automation for development and production.

Experience Level

Mid-level. No explicit years-of-experience stated.

Responsibilities

Primary duties include:

• Participate in bring-up of next-generation AI hardware systems and supporting software infrastructure.
• Debug system-level issues across hardware/software boundaries; reproduce, triage, and determine root causes using logs, telemetry, and diagnostic tools.
• Develop software, test suites, and stress tests for distributed hardware during development and production cycles.
• Build automation frameworks and internal tooling to improve validation and debugging workflows.
• Improve system observability by creating tools that surface failures quickly and accelerate debugging.
• Collaborate closely with hardware engineers to isolate and resolve system integration issues and support hardware qualification toward production readiness.
• Continuously improve engineering workflows related to testing, automation, and reliability.

Requirements

Must-have technical skills and capabilities:

• Strong programming skills in Python and/or C++.
• Excellent debugging and methodical problem-solving skills.
• Solid understanding of operating system fundamentals: processes, threads, memory management, concurrency, 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, telemetry, and log analysis.
• Experience with hardware-adjacent software, system integration, production validation, or infrastructure reliability engineering.

Education Requirements

BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.

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.