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.
We are looking for a Junior Software Development Engineer in Test (SDET) to join the Release Integration Testing (RIT) function within Release & Feature Qualification for AI Inference Core.
The Production Engine for Inference Core — helping turn integrated features into reliable production releases.
You will write software and automation, test new model and platform capabilities, investigate failures across a complex AI system, and help maintain stable master and release branches. You will learn from experienced SDETs and engineers while taking real ownership of scoped qualification and release work.
This is an excellent role for an early-career engineer who enjoys coding, debugging, understanding how systems fit together, and learning quickly. We welcome candidates whose experience comes from internships, research, academic projects, open source, or equivalent hands-on work.
RIT is the bridge between feature qualification and release qualification. Feature teams retain ownership of feature design, feature-level qualification, and feature regression. You will help RIT deliver integration strategy, readiness evidence, cross-stack validation, and first-pass rollout triage for inference-core changes.
RIT mission: Help turn qualified features into production-ready capabilities before release or production becomes the first true integration environment.
Software engineering applied to quality: Build tools, diagnostics, and automation—not just execute manual test cases.
Cross-stack learning: Work across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware.
Inference-path focus: Learn to validate high-risk changes across runtime, host, device programming, memory, scheduling, and model execution.
Production impact: Support branch stability, release readiness, deployment quality, and coordinated rollout across multiple product and release projects.
Team-first growth: Take real debugging ownership with mentorship, communicate clearly, ask for help early, and help the whole team move forward.
Engage with selected inference-core features before qualification completes to understand dependencies, interaction risks, and the required integration scenarios.
Develop, run, and maintain automated tests for models, features, system behavior, integration, regression, and releases across the AI stack.
Collect unit, simulation, benchmark, feature-test, and integration evidence; document gaps; execute cross-stack E2E workflows; and promote durable scenarios into release regression.
Help maintain master and release-branch stability by triaging regression and rollout failures, escalating with clear evidence, identifying owners, validating fixes, and verifying closure.
Write Python, Go, or similar code for test automation, diagnostics, testbeds, data analysis, dashboards, qualification workflows, and release pipelines.
Collaborate with Integration, Core Infra, feature teams, and release owners to reproduce issues, route missing coverage to the correct layer, and support coordinated rollout across multiple product and release projects.
Document test intent and findings, grow toward independent ownership of a test domain, and between active engagements improve automation efficiency, metrics, probes, diagnostics, and roadmap test plans.
Strong software-engineering fundamentals and programming ability in Python, Go, or a similar language.
Experience—through internships, research, academic projects, open source, personal projects, or professional work—building, testing, or debugging software.
Basic understanding of data structures, algorithms, operating systems, networking, or distributed-systems concepts.
Curiosity about how complex systems behave across component boundaries.
Ability to break down problems, form hypotheses, gather evidence, and learn from unexpected results.
Willingness to read unfamiliar code, learn new layers of the stack, and take ownership beyond a narrowly defined task.
Clear communication, collaboration, persistence, learning velocity, and comfort working through ambiguity with guidance.
Coursework or project experience in testing, distributed systems, operating systems, compilers, computer architecture, AI systems, or infrastructure.
Experience building automated tests, test frameworks, CI workflows, developer tools, or data-analysis scripts.
Exposure to software/hardware co-design, hardware accelerators, low-level systems, or performance debugging.
Familiarity with AI infrastructure, model deployment, LLMs, multimodal workloads, containers, or cloud environments.
Experience using logs, metrics, debuggers, profilers, or observability tools to investigate failures.
Internship, startup, research-lab, robotics, systems, or other fast-moving hands-on experience.
Demonstrated zero-to-one initiative through a substantial project, tool, experiment, or open-source contribution.
You deliver reliable automated tests and tools that provide clear, actionable signals.
You can independently triage scoped failures and know when and how to escalate.
Your work helps catch inference-path feature, integration, and branch-stability issues before final qualification or production.
Debug cycles become shorter, coverage ownership becomes clearer, and feature rollout becomes more predictable.
You steadily expand the layers of the stack you can debug and the scope you can own.
You turn feedback and failures into better code, tests, documentation, and engineering judgment.
Teammates trust your follow-through, communication, curiosity, and willingness to help.
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:
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.
