AI Inference Core - Infrastructure SW Engineer

Cerebras Systems

About The Position

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 Team The Core Infrastructure team builds the software systems that power engineering workflows across Cerebras. Our infrastructure coordinates complex work across machines, clusters, development environments, and hardware systems. We build orchestration frameworks, execution engines, scheduling systems, test infrastructure, developer tools, and reusable software platforms that allow engineers to build, test, qualify, and deliver software reliably at scale. These systems are primarily built in Python, but the work goes far beyond scripting or automation. Our frameworks act as the control plane for distributed workflows, managing resources, execution state, concurrency, failures, retries, dependencies, and observability across large and complex environments. About the Role We are hiring a Software Engineer to design and build the core software behind Cerebras engineering infrastructure. You will work on Python frameworks, orchestration systems, distributed execution, scheduling, test infrastructure, and developer tooling. You will help define the architecture and APIs that other engineering teams depend on every day. This role is a strong fit for an engineer who enjoys reading unfamiliar code, understanding how systems fit together, debugging difficult problems, and improving the underlying design rather than applying one-off fixes. We value strong software-engineering fundamentals, independent problem solving, and sound systems thinking more than familiarity with any particular infrastructure product.

Requirements

3+ years of professional software-engineering experience.
Strong proficiency in Python and a solid understanding of the language’s strengths, limitations, and runtime behavior.
Experience designing maintainable software systems, libraries, frameworks, backend services, or developer-facing APIs.
Good judgment around software architecture, abstraction boundaries, design patterns, extensibility, and long-term maintainability.
Understanding of concurrency concepts such as processes, threads, asynchronous execution, synchronization, and shared state.
Foundational understanding of operating systems, including processes, signals, filesystems, resource management, and program execution.
Foundational understanding of distributed-systems concepts such as retries, timeouts, idempotency, partial failure, coordination, and eventual consistency.
Strong debugging and problem-solving skills, including the ability to form hypotheses, gather evidence, and work through unfamiliar systems independently.

Nice To Haves

Experience with Python concurrency technologies such as asyncio, multiprocessing, concurrent futures, or event-driven systems.
Experience building orchestration engines, workflow systems, schedulers, distributed job runners, or control-plane software.
Experience developing test infrastructure or extensions for frameworks such as pytest.
Familiarity with CI systems, build systems, release infrastructure, or developer-productivity tooling.
Experience with Kubernetes, containerized environments, cluster schedulers, or remote execution systems.
BS/MS in Computer Science or a related field, or equivalent practical experience.

Responsibilities

Design, develop, test, and maintain Python frameworks and services used to orchestrate engineering workflows across machines and clusters.
Build reusable abstractions for scheduling, distributed execution, resource management, test execution, workflow planning, and failure recovery.
Define clear APIs, module boundaries, extension points, and data models that allow infrastructure systems to evolve without becoming difficult to maintain.
Reason about concurrency, asynchronous execution, multiprocessing, state management, retries, idempotency, cancellation, and partial failures.
Debug complex issues spanning Python applications, operating systems, processes, filesystems, networking, remote machines, and distributed services.
Write high-quality automated tests and documentation for infrastructure that is expected to be reliable and widely reused.
Partner with platform, CI, release, quality, ML systems, and product engineering teams to understand requirements and translate them into scalable software designs.