AI Engineer

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.

• Design end-to-end agentic solutions and determine when a use case should query a source system directly versus use the unified data model.
• Partner with stakeholders to identify high-value use cases, translate requirements into controlled AI workflows and select AI, conventional automation or no new technology.
• Create reusable architecture patterns for agents, tools, APIs, MCP servers, prompts, evaluations and human-review workflows.
• Produce solution designs, security flows, deployment patterns and technical standards.
• Build AI agents, orchestration services, enterprise applications and reusable platform components.
• Deliver workflows for close and reporting, procurement, forecasting, billing and compliance monitoring where AI adds measurable value.
• Establish secure, primarily read-only AI connections to approved business systems, beginning with NetSuite and extending to adjacent Finance and enterprise platforms as priorities evolve.
• Preserve source-system authentication, authorization, user-level entitlements, rate limits and audit trails.
• Implement citations, evidence links, deterministic checks, exception handling and safe action boundaries.
• Assess business-built or rapidly developed prototypes for value, architecture, security, maintainability and control readiness.
• Refactor or rebuild approved prototypes into tested, monitored and supportable enterprise applications.
• Establish development, test and production environments, release pipelines, incident response and rollback controls.
• Evaluate AI models, agent frameworks, connectors and enterprise platforms on a regular cadence.
• Run structured proofs of concept and assess security, accuracy, integration, scalability, experience, cost and vendor viability.
• Maintain platform standards and recommend adoption, retention, replacement or retirement decisions.
• Create clear documentation, reusable patterns and reference architectures; coach teams on effective agent design, prompts, evaluation practices and safe operating boundaries.
• Establish feedback loops with users and process owners; use adoption, task success, efficiency, trust and support signals to guide iteration.
• Translate Finance, Security, Privacy, SOX and SSDLC requirements into technical architecture and application controls.
• Implement least privilege, segregation of duties, logging, retention, evaluation, change control and audit evidence.
• Require deterministic validation and reconciliation for financially material outputs.
• Support SOX walkthroughs, control testing, audits, risk assessments and remediation while escalating formal approval to control owners.

• 8+ years in software, platform, integration, solution engineering or enterprise applications, including meaningful hands-on production ownership in complex environments.
• Strong Python and/or TypeScript skills; experience with APIs, MCP or comparable tool protocols, enterprise authentication and distributed-system design.
• Practical experience building production AI systems using agents, tool use, retrieval, structured outputs, evaluations and monitoring.
• Practical familiarity with leading LLM platforms and agent frameworks, such as OpenAI, Anthropic, Gemini, LangChain, Semantic Kernel or comparable technologies, including prompt and context engineering.
• Strong solution-architecture judgment across security, reliability, performance, cost, observability and supportability.
• Working knowledge of enterprise Finance processes such as general ledger, close, reporting, procure-to-pay, order-to-cash, forecasting and management reporting.
• Working knowledge of compliance-by-design, including access, segregation of duties, change management, interfaces, automated controls, completeness and accuracy, and audit evidence.
• Ability to communicate with engineers, Finance leaders, control owners, Security and executives.

• 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.