AI Inference Core - Software Integration Engineer

Responsibilities

• Turn new inference ideas and features into integrated, working capabilities across the Cerebras platform.
• Take high-value projects from zero to one, from incomplete ideas or prototypes to working, validated capabilities.
• Integrate and validate software components spanning AI frameworks, runtime, compiler, kernels, distributed systems, and hardware.
• Drive complex cross-component projects from problem definition through integration, validation, and release readiness.
• Collaborate with software and hardware engineers on co-design trade-offs and 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.
• Manage changing priorities and drive ambiguous situations toward concrete outcomes.
• Identify bottlenecks, failure modes, edge cases, and integration gaps affecting inference correctness, performance, or delivery.
• Capture lessons from urgent work to improve automation, diagnostics, documentation, and repeatable practices.
• Help teammates move faster, make better decisions, and close difficult problems together.

Requirements

• Strong software-engineering fundamentals and programming ability in Python, C++, Go, or a similar language.
• Ability to break down ambiguous technical problems, form hypotheses, gather evidence, and drive issues to resolution.
• Experience building or debugging software systems through professional work, internships, research, academic projects, open source, or equivalent hands-on work.
• 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 stay effective during uncertainty, rapid change, and accelerated delivery.
• Clear communication and strong collaboration across disciplines and experience levels.

Preferred Skills

• Experience in a startup or similarly fast-moving, resource-constrained engineering environment.
• Experience taking an ambiguous problem, early idea, or prototype from zero to 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.

Work Arrangement

• Hybrid schedule with in-office presence three days per week.
• Fully remote work is not available.
• Office locations: Sunnyvale, CA or Toronto, ON.

Benefits

• Opportunity to build a breakthrough AI platform beyond the constraints of GPUs.
• Opportunities to publish and open source cutting-edge AI research.
• Work on a high-performance AI supercomputer.
• Startup vitality with job stability.
• Non-corporate work culture that respects individual beliefs.