AI Inference Core - Software Integration Engineer
Cerebras is hiring an AI Inference Core - Software Integration Engineer. Level rates it ; you can apply on Cerebras.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include OpenAI and AI research.
- Level lists 121 other open roles at Cerebras.
AI in this role
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.
About the Role
We are looking for a Software Integration Ninja to join the AI Inference Core team at Cerebras. This team sits at the intersection of AI infrastructure, distributed systems, compilers, runtimes, kernels, and hardware/software co-design.
The Innovation Engine for Inference Core — turning ideas into reality.
You will help take ambitious ideas from concept to working reality across the Cerebras inference stack. You will integrate and validate cross-component projects of high complexity, often on accelerated timelines, and work directly with engineers across AI, runtime, compiler, kernel, systems, and hardware teams.
A Special Task Force, Not a Typical Engineering Role
Zero-to-one mission: Take incomplete ideas and early prototypes all the way to working, validated capabilities.
Cross-stack complexity: Move across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware—not just one component or codebase.
Accelerated and dynamic cadence: Expect focused daily syncs, rapidly changing priorities, and periods of intense integration work. This is not a role for engineers seeking strictly regular, predictable work hours.
Comfortable on the hot seat: Take ownership when the path is unclear, make sound decisions with incomplete information, and stay effective when timelines are tight.
Bias for action: At critical moments, the mindset is “No Process, No Documentation, Just Get Stuff Done!!!” Cut through unnecessary ceremony, deliver the result, and then turn what you learned into better automation, diagnostics, documentation, and repeatable practices.
Team multiplier: Always push the work forward while raising the pace, clarity, and effectiveness of the people around you. We want people who bring the team with them—not lone heroes.
We prefer candidates with experience in software/hardware co-design or other complex systems, but that experience is not required. We welcome exceptional junior engineers who are eager to learn, do not shy away from ambiguity or hard work, and can demonstrate strong fundamentals, curiosity, ownership, and persistence.
What You Will Do
Turn new inference ideas and features into integrated, working capabilities across the Cerebras platform.
Take high-value projects from zero to one—from an incomplete idea or prototype to a working, validated capability.
Integrate and validate software components spanning AI frameworks, runtime, compiler, kernels, distributed systems, and hardware.
Drive cross-component projects of high complexity from problem definition through integration, validation, and release readiness.
Collaborate closely with software and hardware engineers to make co-design trade-offs and resolve system-level issues.
Investigate and debug difficult failures across large-scale AI workloads, distributed software, infrastructure, and hardware boundaries.
Work effectively on accelerated timelines while keeping technical risks, dependencies, and decisions visible.
Participate in focused daily syncs, manage rapidly changing priorities, and drive ambiguous situations toward concrete outcomes.
Identify bottlenecks, failure modes, edge cases, and integration gaps that affect inference correctness, performance, or delivery.
Capture the essential lessons from urgent work so the next integration is faster and less chaotic.
Create momentum beyond your own work by helping teammates move faster, make better decisions, and close difficult problems together.
Minimum Skills & Qualifications
Strong software-engineering fundamentals and programming ability in Python, C++, Go, or a similar language.
Demonstrated ability to break down ambiguous technical problems, form hypotheses, gather evidence, and drive issues to resolution.
Experience—through professional work, internships, research, academic projects, open source, or equivalent hands-on work—building or debugging software systems.
Curiosity about how complex systems behave across component boundaries.
Willingness to read unfamiliar code, learn new layers of the stack, and take ownership beyond a narrowly defined area.
Ability to work hard and stay effective during periods of uncertainty, rapid change, and accelerated delivery.
Clear communication and strong collaboration across disciplines and levels of experience.
Preferred Skills
Experience in a startup or similarly fast-moving, resource-constrained engineering environment.
Demonstrated experience taking a project from zero to one: turning an ambiguous problem, early idea, or prototype into a working and reliable capability.
Experience with software/hardware co-design, hardware accelerators, compilers, kernels, runtimes, or low-level systems.
Experience debugging complex systems, distributed software environments, or large-scale compute clusters.
Experience with AI infrastructure, model deployment, LLMs, or multimodal workloads.
Exposure to performance debugging, profiling, observability, or failure analysis.
Familiarity with microservices, containers, cluster orchestration, cloud infrastructure, or high-performance computing.
Track record of driving cross-team projects from an incomplete idea to a reliable working result.
Location
This role follows a hybrid schedule and requires in-office presence three days per week. Fully remote work is not available.
Office locations: Sunnyvale, CA or Toronto, ON.
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.
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How we rate this
AI Inference Core - Software Integration Engineer at Cerebras rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a research question you investigated. What did you find?
- Walk me through how you've used OpenAI in your day-to-day work.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI Research and OpenAI. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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