
ML Systems Integration Engineer
Required skills
- AI chip
- AI hardware
- AI systems
- Python
- C++
- Linux
- operating systems
- computer architecture
- system integration
- root cause analysis
Nice to have
- automation frameworks
- internal tooling
- test infrastructure
- distributed systems concepts
- large-scale systems
- complex infrastructure
Role summary
Cerebras Systems is seeking an ML Systems Integration Engineer to work on next-generation AI hardware systems and supporting software infrastructure. Responsibilities include debugging complex system-level issues, building automation frameworks, developing testing software, and collaborating with hardware engineers. The role requires strong programming skills in Python and/or C++, deep understanding of operating systems and computer architecture, and excellent debugging and analytical skills within a Linux environment. Experience with distributed systems, automation, and system observability is preferred.
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Responsibilities
Participate in bring-up of next-generation AI hardware systems and supporting software infrastructure.
Debug complex system-level issues spanning hardware and software interactions.
Investigate failures occurring during system bring-up and identify root causes using logs, telemetry, and diagnostic tools.
Build automation frameworks and internal tooling that improve system validation and debugging workflows.
Develop software used to test, validate, and stress distributed hardware systems during development and production cycles.
Collaborate closely with hardware engineers to isolate and resolve system integration issues.
Improve system observability by building tools that surface failures quickly and accelerate debugging.
Reproduce, triage, and diagnose difficult issues that arise during early hardware deployment.
Support validation and qualification of new hardware generations as systems move toward production readiness.
Continuously improve internal engineering workflows related to debugging, testing, and automation.
Skills & Qualifications
BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or related technical field.
Strong programming skills in Python and/or C++.
Excellent debugging and problem-solving skills with ability to investigate complex technical issues methodically.
Solid understanding of operating systems fundamentals (processes, threads, memory management, concurrency, IPC).
Experience working in Linux development environments.
Understanding of computer architecture and interactions between hardware and software systems.
Strong analytical thinking and ability to break down complex system failures into actionable root causes.
Ability to work effectively across multiple engineering teams and collaborate in highly technical environments.
Strong communication skills and willingness to work on ambiguous technical problems.
Preferred Skills & Qualifications
Experience building automation frameworks, internal tooling, or test infrastructure
Familiarity with distributed systems concepts
Experience debugging large-scale systems or complex infrastructure environments
Understanding of networking fundamentals and communication between distributed systems
Experience working with hardware-adjacent software or system integration environments
Familiarity with performance analysis, system telemetry, and log analysis
Exposure to production systems validation or infrastructure reliability engineering
Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
Build a breakthrough AI platform beyond the constraints of the GPU.
Publish and open source their cutting-edge AI research.
Work on one of the fastest AI supercomputers in the world.
Enjoy job stability with startup vitality.
Our simple, non-corporate work culture that respects individual beliefs.
Find out more about what it's like to work at Cerebras here!
Apply today and become part of the forefront of groundbreaking advancements in AI!
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.
Similar roles
AI Inference Core - Infrastructure SW EngineerCerebras · Sunnyvale, CA · Onsite
Physical Security Lead, Manufacturing OperationsCerebras · Sunnyvale, CA · Hybrid
Senior Customer Integration EngineerUnite Us · Remote · Remote- Machine Maintenance and Integration EngineerHelion · Everett, WA · Onsite
- MAUTOSAR BSW Platform Integration EngineerMARTIN Technologies · Allen Park, MI · Onsite
- Robotics Integration Engineer, Tech LeadApplied Intuition · Sunnyvale, CA · Onsite
