Why now

Demand for Cerebras inference is high and climbing. We need ever-more power-ready data center capacity to meet demand for the world’s fastest inference solution. Project facts often change as providers work through power, site, capital, design, security, and schedule issues. This work shapes customer delivery, capital use, and risk for years. The job demands a hands-on deal leader who can separate real capacity from optimistic claims and keep priority transactions on track.

Role at a glance

• Own the data center capacity pipeline across North America, Europe, and other priority markets.
• Source and evaluate data center providers, developers, colocation sites, expansion projects, and partner-led capacity.
• Lead commercial work from first qualification through diligence, internal approval, customer review, signature, and handoff.
• Diligence power, site control, permits, design, security, operations, financing, and schedule claims.
• Ensure compliance with regional regulations, permitting requirements and mitigate reputation risk.
• Accountable for aligning business functions (legal, finance, procurement, etc.), internal delivery teams (networking, infrastructure, operations, security, etc.), executive, and customer teams to qualify and execute partnerships.
• Build a team to execute at “the speed of light”

What you will build

• A repeatable data center acquisition system that turns credible supply into signed capacity.
• A qualified pipeline with clear views of location, capacity, timing, provider, commercial status, diligence status, and risk.
• Diligence standards that test provider claims before Cerebras commits company time or capital.
• Executive and customer deal summaries that state the facts, risks, decisions, and next steps.
• Term and negotiation standards that give teams a shared baseline.
• Assets and rituals that expose blockers early and keep owners accountable.
• Deal evaluation framework and metrics (inclusive of total cost of ownership) enabling high velocity decision making

What you will own

• Market coverage: Build relationships with data center providers, developers, infrastructure partners, power partners, and other sources of capacity.
• Opportunity qualification: Decide which opportunities deserve company time, technical review, legal work, customer review, and capital.
• Commercial work: Lead negotiations and contract work with clear business goals, risk tradeoffs, and timelines.
• Diligence: Collect facts from internal experts and outside parties across power, site, design, security, operations, finance, legal, and customer requirements.
• Risk calls: Identify the risks that matter, ask providers to prove their claims, and make clear recommendations.
• Customer review: Prepare summaries, surface open issues, manage review cycles, and make sure customer requirements shape the deal before signature.
• Path to close: Maintain owner-based close plans, move decisions through the company, and prevent stalled deals.
• Executive updates: Give concise answers on what is real, what blocks signature, who owns the next step, and what requires a decision.

What success looks like in the first 6 to 12 months

• Cerebras has a current, prioritized, and trusted view of all active capacity opportunities.
• Priority deals have owners, open issue lists, close plans, and escalation paths.
• Cerebras signs strong opportunities and pauses or kills weak ones based on facts.
• Providers prove power, site, financing, technical, security, and schedule claims before Cerebras commits.
• Internal and customer reviews move faster because deal materials state the facts, risks, and decisions.
• Teams reuse diligence standards and negotiation standards across deals.
• Cerebras can forecast capacity by site, region, provider, and delivery window.
• Leaders know which capacity is real, which deals will close, and which risks need action.

What we look for

• We want a data center deal leader with infrastructure judgment, commercial skill, and a bias for facts. You have closed complex infrastructure transactions and can work credibly with data center, power, finance, legal, technical, security, operations, and customer teams.
• 10+ years of relevant experience in hyperscale data center acquisition, cloud infrastructure sourcing, colocation leasing, site selection, data center development, infrastructure business development, power or site acquisition, strategic sourcing, or infrastructure finance.
• You have led high-value data center, cloud infrastructure, or critical infrastructure transactions.
• Strong grasp of data center capacity drivers: power, cooling, network, site readiness, permits, security, operations, provider credibility, and delivery timing.
• Credibility with legal and finance leaders on complex commercial risk.
• Credibility with technical, security, and operations experts. You can turn their input into business decisions.
• Clear executive communication. You can summarize a complex deal in one page, name the decision, and recommend the path.
• Strong ownership. You create structure, assign owners, escalate early, and close loops without perfect process.
• Sound risk judgment. You know when to push, when to slow down, and when to walk away.

Ways to stand out

• Experience at a hyperscaler, AI infrastructure company, cloud provider, data center developer, wholesale colocation provider, infrastructure investor, or power infrastructure company.
• Experience with AI, GPU, HPC, or other high-density compute environments.
• Familiarity with North American and European data center markets.
• You have assessed utility power, interconnection status, temporary power, backup power, or power-constrained markets.
• You have worked on deals that require customer review, confidentiality controls, or public-company disclosure discipline.
• You have built acquisition processes, diligence workflows, executive reports, or commercial standards from scratch.
• CDCDP or similar data center credentials.

Location

In-person at Cerebras headquarters in Sunnyvale, California. Expect travel to sites, data center providers, developers, partners, and customer meetings.

Cerebras Systems Inc. has multiple openings for Member of Technical Staff (Software Engineer)

Title: Member of Technical Staff (Software Engineer)

Job Duties

• Implement infrastructure to support high-performance, low-latency inference service.
• Deploy and configure Kubernetes services to ensure scalability and reliability of inference workloads.
• Optimize resource allocation and auto-scaling policies to handle variable inference demand while minimizing operational costs.
• Integrate inference services with containerized environments using Docker and Kubernetes for orchestration.
• Ensure high availability and fault tolerance by implementing multi-region deployments and disaster recovery strategies.
• Develop Python-based scripts and APIs to streamline data preprocessing, inference execution, and post-processing for real-time inference tasks.
• Collaborate with machine learning engineers to validate inference accuracy and performance against functional and latency requirements.
• Triage and resolve defects in the service by analyzing logs, metrics, and distributed traces.
• Debug issues related to model deployment, container orchestration, or networking configurations, documenting steps to reproduce and root-cause defects.
• Collaborate with cross-functional teams to address performance regressions, scalability issues, or integration failures in the inference pipeline.
• Develop automated scripts to detect and mitigate common failure modes, improving system reliability.
• Author detailed technical documentation for infrastructure configurations, inference workflows, and APIs, ensuring clarity for internal teams and external customers.
• Work with product management and user experience teams to define requirements for inference service interfaces, including configuration, monitoring, and event logging.
• Document and track defects, enhancements, and release notes using tools like Jira and Git, ensuring version control and traceability.
• Participate in release planning and prioritization discussions to align infrastructure development with customer needs and business objectives.

Minimum Requirements:

Master’s degree or foreign equivalent degree in Computer Science, or a related field and 1 year of experience as Software Developer, Student/Intern (Software Developer), Member of Technical Staff (Software Engineer), Software Engineer, or a related occupation required. Employer accepts full-time or equivalent part-time experience gained before, during or after graduate studies.

Required Skills:

• Docker and Kubernetes;
• Java or C++;
• ActiveMQ and Kafka;
• Python or Groovy;
• JavaScript or TypeScript;
• Linux;
• SQL, OracleDB, and Redis; and
• Git

Additional Information:

Employer’s name: Cerebras Systems Inc. 

Job site : 1237 E Arques Avenue, Sunnyvale, CA 94085

Telecommuting permitted

Salary Range: $169,600.00 per year to $175,000.00 per year

If you are interested in applying for this position, please apply online on this web page or mail resume to HR at Cerebras Systems Inc., 1237 E Arques Avenue, Sunnyvale, CA 94085. Please reference Job # 146 on resume or cover letter.

 Cerebras Systems Inc. has multiple openings for Sr. Technical Staff.

Title: Sr. Technical Staff

Job Duties:

• Post silicon validation of Cerebras Wafer Scale Engines. Test and debug issues on new silicon. 
• Test, analyze, and characterize high-speed serial interfaces to verify compliance with hardware specifications, record performance data, and recommend design modifications to optimize functionality.
• Work with the silicon and operations team to test, bring-up and run burn-in on wafers scale systems.
• Support manufacturing operations to utilize the wafer bring up flow. Perform wafer bring-ups, diagnose and debug problems encountered.
• Develop and implement hardware to ensure compliance with design specifications.
• Collaborate with hardware design engineers and system software engineers to review specifications and to recommend changes that will improve the quality and verifiability of the hardware designs.
• Create and maintain automated regression test scripts, using Python and/or bash, that ensure that all tests are run and pass after each change to the design, testbench, tests, or reference model.
• Work with system team members to diagnose system related failures. Understand the key system interfaces to FPGA’s, power and cooling, and apply that knowledge to the debug of silicon features.
• Development of debug tools in Python to program and analyze the behavior of the Wafer Scale Engine.
• Development of wafer bring up flow utilizing Python and shell scripts to capture the steps required to bring up a wafer in a logical easy to use flow.
• Documentation of issues found, tools and flow.

Minimum Requirements:

Master’s degree or foreign equivalent degree in Electrical Engineering, Computer Engineering, or a related field and 3 years of experience as Application Engineer, Sr. Technical Staff, Hardware Engineer, or a related occupation required.

Required Skills:

• Electrical Signal Integrity Analysis;
• Hardware Bring-up & Debug;
• Functional and Electrical characterization;
• Test automation using scripting language; and
• High Speed Interfaces & Protocols including Ethernet, CPRI, or Interlaken.

Additional Information:

Employer’s name: Cerebras Systems Inc. 

Job site : 1237 E Arques Avenue, Sunnyvale, CA 94085

Telecommuting permitted.

Salary Range: $250,000.00 per year to $275,000.00 per year

If you are interested in applying for this position, please apply online on this web page or mail resume to HR at Cerebras Systems Inc., 1237 E Arques Avenue, Sunnyvale, CA 94085. Please reference Job # 145 on resume or cover letter.

Cerebras Systems Inc. has multiple openings for Sr. Member of Technical Staff

Title: Sr. Member of Technical Staff

Job Duties:

• Design and develop software features that support system resiliency and high availability, including automated recovery mechanisms and fault-tolerant architecture across distributed environments.
• Develop and maintain cloud-based deployment workflows for AI inference software using AWS tools and services to support low-latency and scalable system performance.
• Develop Python-based scripts and APIs to streamline data preprocessing, inference execution, and post-processing for real-time inference tasks.
• Use parallel programming techniques (e.g., multi-threading, asynchronous processing) to maximize resource efficiency on AWS compute instances.
• Develop software components to support visualization and analysis of system performance metrics, enhancing the monitoring and usability of inference services. ⠀
• Develop inference software in Docker containers and define Kubernetes orchestration strategies that ensure software reliability and efficient scaling.
• Develop automated scripts to detect and mitigate common failure modes, improving software system reliability.
• Debug issues related to model deployment, container orchestration, networking configurations, documenting steps to reproduce and root-cause defects.
• Triage and resolve defects in the software service by analyzing logs, metrics, and distributed traces using tools like AWS CloudWatch, Grafana, or custom Python scripts.
• Work with product management and user experience teams to define requirements for inference service interfaces, including configuration, monitoring, and event logging.
• Author detailed technical documentation for infrastructure configurations, inference workflows, and APIs, ensuring clarity for internal teams and external customers.
• Document and track defects, enhancements, and release notes using tools like Jira and Git, ensuring version control and traceability.

Minimum Requirements:

Master’s degree or foreign equivalent degree in Computer Science, or a related field and 18 months of experience as Information Security Analyst, Software Engineer, Sr. Member of Technical Staff, IT Senior Applications Engineer, or a related occupation required.

The required experience must include 18 months of experience with the following:

• Infrastructure-as-Code and deployment automation:Terraform, AWS CloudFormation, AWS CDK, and Ansible;
• Containerization and orchestration:Docker, Kubernetes, AWS EKS, AWS Elastic Container Service (ECS), AWS Fargate, and Helm;
• Compute and serverless services: AWS EC2, AWS Lambda functions, and Auto Scaling Groups;
• Monitoring, logging, and distributed tracing: AWS CloudWatch, AWS X-Ray, ELK (Elasticsearch, Logstash, Kibana), Prometheus, and Grafana;
• Programming languages and frameworks: Python, Node.js, JavaScript, and Flask;
• Data storage and caching: PostgreSQL, Redis, and NFS; and
• CI/CD and version control: Jenkins and Git

Additional Information:

Employer’s name: Cerebras Systems Inc. 

Job site : 1237 E Arques Avenue, Sunnyvale, CA 94085

Telecommuting permitted

Salary Range: $230,000.00 per year to $250,000.00 per year

If you are interested in applying for this position, please apply online on this web page or mail resume to HR at Cerebras Systems Inc., 1237 E Arques Avenue, Sunnyvale, CA 94085. Please reference Job # 142 on resume or cover letter.

About The Role

The Inference Core Platform group is at the heart of Cerebras' mission to deliver the world’s fastest AI inference. Our team builds the foundational software and hardware infrastructure that powers low-latency, high-speed, high-throughput deployment on the Cerebras Wafer-Scale Engine (WSE). We are responsible for the full stack—from model compilation and scheduling down to custom hardware kernels and driver development.

The ML Performance Benchmarking team plays a pivotal role in shaping the performance and scalability of AI inference on one of the most advanced computing systems ever built. We drive the bring-up of core inference capabilities and deliver performance improvements at every stage of development – from early prototyping to production deployment.

We're looking for passionate engineers to join us in redefining the limits of AI inference. If you thrive on building systems that measure, analyze, and optimize performance at scale, this is your opportunity to make a transformative impact on the future of AI.

Scope of the team includes:

• Core Inference Observability – Design and implement end-to-end telemetry systems across the software stack, providing deep visibility into inference performance and enabling rapid iteration before and after deployment.
• Benchmarking Infrastructure – Architect, build, and scale the automation that generates, analyzes, and visualizes performance data used to inform business decisions across engineering and leadership.
• Performance Analysis – Dive deep into system behavior, dissect performance bottlenecks, and deliver actionable insights that directly influence which features ship and how they evolve.
• Feature Integration – Partner closely with Core Platform teams to define rigorous testing methodologies that validate inference features for peak performance.

Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Engineering, Systems Engineering, or a related field.
• Proficiency in Python and/or C++ programming.
• Proven experience in building and scaling automated infrastructure.
• Strong background in throughput and performance optimization techniques, especially in complex, large-scale systems.
• Excellent problem-solving skills and a strong analytical mindset.
• Demonstrated ability to dive deep into new domains.
• Ability to work in a fast-paced, ambiguous, and collaborative environment.

Preferred Skills & Qualifications

• Familiarity with problem-solving at the intersection of hardware and software.
• Hands-on experience with AI workloads and architectures is a plus.

Location

• On-site or hybrid at our Toronto office

#LI-WA1

About The Role

We are hiring a Senior Performance Engineer to join our Product team. You are an expert on state-of-the-art inference performance and will serve as our resident expert on how Cerebras stacks up against alternative inference providers on both price and performance. This role sits at the intersection of performance benchmarking from first principles and competitive intelligence. The role has two core pillars:

1. Performance Benchmarking
   You will build, run, and maintain reproducible benchmarks that measure Cerebras inference performance for real customer workloads. This includes metrics like tokens per second, time to first token, latency under concurrency, and total cost of ownership (TCO). 
   
2. Competitive Pricing Intelligence
   You will build and maintain a living model of competitor pricing across the AI inference landscape, including cloud providers, custom silicon vendors, and inference API platforms. You will work directly with our Sales and Product teams to translate this intelligence into pricing recommendations for enterprise contracts, ensuring Cerebras offers a compelling value proposition for every customer.

This role requires deep, hands-on fluency with open-source inference stacks (vLLM, SGLang, TensorRT-LLM), GPU kernel-level optimization toolchains (CUDA, Triton), and an intuitive understanding of how transformer architecture decisions—attention mechanisms, model sizing, quantization, KV-cache strategies—interact with the realities of GPU memory hierarchies and compute budgets. 

Responsibilities

• Design standardized benchmark suites for inference workloads (code generation, summarization, multi-turn conversation, agentic tool use) that enable fair, reproducible comparisons. 
• Stay current with GPU optimization communities (CUDA, Triton, TensorRT) and evaluate how new kernel fusions, flash-attention variants, and quantization techniques shift performance ceilings. 
• Build and continuously update a competitive pricing model covering token-based pricing, throughput-based pricing, and enterprise contract structures across major inference providers. 
• Monitor industry announcements, pricing changes, and new product launches. Synthesize findings into actionable briefs for the Sales and Product teams. 
• Partner with Sales to build deal-specific competitive analyses showing total cost of ownership and performance advantages for enterprise prospects. 
• Collaborate with Product and Engineering to identify where competitors are closing gaps or where Cerebras has underappreciated advantages. 
• Track third-party benchmarking sources (Artificial Analysis, InferenceX) and ensure Cerebras is well-represented and accurately measured. 

Skills & Qualifications

Required 

• Deep practical experience with state-of-the-art open-source inference frameworks like vLLM, SGLang, or TensorRT-LLM. 
• 5+ years of experience in ML systems, ML research engineering, or high-performance computing. 
• Strong understanding of LLM inference economics: tokens, throughput, latency, batch sizes, precision trade-offs, and how these translate to customer cost. 
• Strong understanding of transformer model architecture internals such as attention mechanisms (MHA, MQA,GQA, MLA, DSA, MHA) and KV-cache management, and how each affects memory and compute profiles. 
• Self-directed and resourceful.  

Preferred 

• Background in ML research (publications or significant open-source contributions) with a systems or efficiency focus. 
• Contributions to open-source inference or kernel optimization projects. 
• Excellent communication skills. You will collaborate with executives, write for engineers, and create materials for sales leaders. 

About The Role

As a member of our tight knit physical design team, you will be working on the design and analysis of 3D integrated products. This role involves a combination of traditional ASIC/SoC physical design skills, packaging, power, clock and cooling analysis. You will work closely with the architecture and RTL team to do R&D on novel concepts for 3D integration. 

Skills and Qualifications

Required 

•     10+ years of physical design/verification experience. 
•     Strong knowledge of block level and full-chip physical verification methodology. 
•     Expert at optimizing for the best power/performance and area.
•     Experience with the complete physical design flow. Knowledge of Synopsys tool suite is a plus. 
•     Expert with ICV or Calibre tools resolving block and full-chip DRC and LVS issues. 
•     Expert with IR/EM analysis and resolution.
•     Strong ability in scripting languages like Tcl and Python. Ability to make flow enhancements. 
•     Demonstrated ability to work with RTL teams to optimize for physical design.
•     Knowledge of 2.5D or 3D packaging solutions.
•     Must have experience with 3d physical design, 3d die stacking, 3d chip design, die-to-die or wafer-to-wafer.

Preferred    

•     Experience doing full chip floor planning and integration.   
•     Knowledge of clock distribution.
•     Knowledge of cooling analysis.

About The Role

We are seeking an experienced IT SRE Team Lead to build and run the reliability function for Cerebras' internal technology estate. 

The IT SRE Team Lead will be responsible for the availability, performance, and operational quality of the systems Cerebras employees rely on every day, including identity, endpoint management, collaboration, SaaS, and internal networking. The right candidate will bring a software engineering mindset to IT operations, treating corporate infrastructure as code, with measurable SLOs, automated remediation, and a ruthless focus on eliminating toil. 

You will build and lead a small, high-leverage team of engineers who build tooling, write automation, and respond when things break. You will partner closely with the security, networking, and infrastructure teams to make sure the internal environment stays fast, stable, and secure as the company scales. 

Responsibilities 

• Define and own the reliability strategy for internal IT systems, including SLOs, error budgets, and operational health reporting. 
• Build and lead a team of IT SRE engineers focused on automation, observability, and incident response for corporate systems. 
• Design and implement automation to eliminate manual IT work across provisioning, access management, patching, and lifecycle operations. 
• Instrument internal services and SaaS integrations with monitoring, alerting, and on-call workflows. 
• Run incident response for IT outages, including root cause analysis and durable remediation. 
• Drive infrastructure-as-code and GitOps practices across IT-owned systems. 
• Partner with security and networking teams on identity, access, and network reliability. 

Skills And Qualifications   

• Minimum 8 years of experience in SRE, DevOps, or IT engineering roles, with at least 2 years in a leadership capacity. 
• Direct hands on experience with AI coding tools, building and deploying AI agents for triage and bug fixes. 
• Strong software engineering background with hands-on experience in Python, Go, or similar, and comfort writing production-grade automation. 
• Deep experience with identity platforms (Okta, Entra), endpoint management (Jamf, Intune), and SaaS integration patterns. 
• Hands-on experience with infrastructure-as-code tools (Terraform) and CI/CD pipelines applied to IT systems. 
• Proven track record of running on-call rotations, defining SLOs, and driving operational maturity in a fast-moving environment. 
• Experience supporting highly technical engineering populations where uptime and speed both matter. 
• Strong organizational skills with the ability to multitask and prioritize. 
• Detail-oriented with the ability to anticipate the needs of customers and internal stakeholders. 
• Proactive, adaptable, and able to thrive in a rapidly changing environment. 
• Excellent verbal and written communication skills. 

About The Role

As our team grows Cerebras is looking for a world class physical design engineer. We are looking for a strong learner who can learn how we do PD and integration of our wafer scale design.
As a member of our tight knit physical design team, you will perform a variety of physical design tasks such as synthesis, place and route, timing and block closure / sign off. You will be involved in all aspects of physical design and implementation. You will work closely with the RTL team and with full-chip integration of these blocks.

Skills and Qualifications

• 7+ years of physical design/verification experience.
• Strong experience in block/subsystem timing closure.
• Strong ability to learn and grow with the team.
• Strong knowledge of block level and full-chip physical verification methodology.
• Expert at optimizing for the best power/performance and area.
• Experience with the complete physical design flow. Knowledge of Synopsys tool suite is a plus.
• Expert with ICV or Calibre tools resolving block and full-chip DRC and LVS issues.
• Expert with IR/EM analysis and resolution.
• Good understanding of full chip floor-planning and integration. 
• Strong ability in scripting languages like Tcl and Python. Ability to make flow enhancements.
• Demonstrated ability to work with RTL teams to optimize for physical design.
• Ability to take on a leadership role after ramping up on wafer scale design

Job Summary

The Sourcing Director – Critical Components is responsible for developing and executing global sourcing strategies to secure high-quality, cost-effective critical components and materials. This role ensures supply chain continuity, minimizes risk, and drives innovation by leveraging market analysis, supplier relationship management, and advanced negotiation tactics. The manager collaborates with cross-functional teams to align procurement activities with organizational goals, optimize procurement processes, and enhance supplier relationships.

Key Responsibilities:

• Strategic Sourcing: Develop and implement comprehensive sourcing strategies for critical components, aligning with long-term business objectives and ensuring competitive advantage.
• Supplier Management: Build and maintain strong relationships with key suppliers, conduct regular performance reviews, and manage contracts to ensure terms are met and risks are mitigated.
• Cost Optimization: Identify opportunities for cost reduction, quality improvement, and process efficiency through market analysis and innovative sourcing solutions.
• Risk Management: Monitor supply chain risks, diversify supplier base, and implement contingency plans to ensure uninterrupted supply of critical components.
• Cross-Functional Collaboration: Work closely with finance, operations, legal, and engineering teams to harmonize procurement activities with overall corporate strategy.
• Commodity & Component Expertise: Oversee commodity management, finished goods, and component sourcing, with a focus on critical and high-impact materials.
• Process Standardization: Provide guidance on standardized RFP/bid and contract processes, ensuring compliance and best practices across the organization.
• Performance Metrics: Track and report on key performance indicators related to cost savings, quality, delivery, and supplier performance.

Qualifications & Skills:

• Education: Bachelor’s degree in Business, Supply Chain Management, Engineering, or related field; Master’s degree or MBA preferred.
• Experience: 10+ years in sourcing, procurement, or supply chain management, with at least 3 years at a senior level in a multinational environment.
• Industry Knowledge: Deep understanding of manufacturing processes, import sourcing practices, and global supply chain dynamics.
• Skills: Strong negotiation, analytical, leadership, and communication skills. Proficiency in contract law, market analysis, and supplier relationship management.
• Technical: Experience with LEAN manufacturing, supply management, and procurement software/tools.

Preferred Competencies:

• Ability to interpret complex data and drive data-informed decision-making.
• Track record of driving cost efficiencies and process improvements in procurement.
• Experience in managing international supplier relationships and global sourcing initiatives.

Location: Sunnyvale, CA

The base salary range for this position is $200,000 to $240,000 annually.  Actual compensation may include bonus and/or equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role

We are seeking an experienced Manufacturing Linux / Network Engineer to design, implement, and maintain robust IT and network infrastructure across our manufacturing facilities. The ideal candidate brings deep expertise in Linux systems administration (Red Hat / Rocky Linux), network security (Palo Alto firewalls), storage infrastructure, CI/CD pipelines (Jenkins), and infrastructure automation (Ansible). This role sits at the intersection of enterprise IT and plant-floor operations, and is critical to delivering the high availability, security, and performance that modern manufacturing environments demand.

Responsibilities

• Design, deploy, and maintain LAN/WAN network infrastructure spanning manufacturing plants, warehouses, cloud providers, and corporate sites.
• Implement and manage high-speed core switching at 10G and 100G using Arista, Juniper, and other enterprise switching platforms; ensure scalable, resilient fabric design.
• Configure, troubleshoot, and optimize Layer 2/3 networking (LACP, VLANs, BGP) and security controls (Palo Alto firewalls, VPNs, NAC, IDS/IPS); manage ISP link failover and path redundancy.
• Deploy, configure, and maintain Linux servers (Red Hat / Rocky Linux) using automation tools including Ansible, MAAS, Foreman, and custom scripting to ensure consistent, repeatable provisioning.
• Monitor network and system performance — uptime, latency, bandwidth utilization, and capacity — across all sites; proactively detect and resolve issues before they impact
• Design and maintain redundancy, failover, QoS, and traffic engineering strategies to support 24/7 manufacturing operations and minimize unplanned downtime.
• Manage structured cabling, Wi-Fi 6/6E wireless infrastructure, and ruggedized networking hardware suited for plant-floor environments.
• Partner with engineering, automation, OT, and IT security teams to ensure secure and reliable connectivity for production and operational systems.
• Lead and contribute to greenfield and brownfield IT/OT modernization projects, including network redesigns for new equipment rollouts and facility expansions.
• Own network documentation including topology diagrams, IP address management (IPAM), and standard operating procedures; keep them current as infrastructure evolves.
• Provide Tier 2/3 support for network and Linux system incidents at manufacturing sites; participate in on-call rotation for production-critical issues.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Electrical Engineering, or a related field.
• 4+ years of experience in network and Linux infrastructure engineering, preferably in a manufacturing or industrial environment.
• Deep Linux expertise with Rocky Linux / RHEL, including administration of core infrastructure services: DNS, DHCP, and network storage (NFS).
• Hands-on experience with Palo Alto Networks firewalls, including policy management, threat prevention, and configuration of active/standby ISP failover links.
• Demonstrated ability to automate infrastructure at scale; proven track record applying Infrastructure as Code (IaC) best practices using Ansible, Terraform, or equivalent tooling. 
• Strong knowledge of networking fundamentals: TCP/IP, VLANs, routing protocols (OSPF, BGP), switching, and network security.
• Experience with enterprise network vendors including Cisco, Arista, and Juniper; familiarity with ruggedized industrial switches (Hirschmann, Cisco IE series, or similar).
• Knowledge of cybersecurity best practices aligned with NIST frameworks. 
• Experience with wireless networking (Wi-Fi 6, cellular/private LTE) in industrial or plant-floor settings. 
• Experience with network monitoring and observability platforms (Grafana, Zabbix, or similar) and on-call alerting integrations such as PagerDuty.
• Ability to review data center and plant-floor physical designs; experience collaborating with cabling vendors and server rack build teams to implement structured cabling best practices.
• Comfortable working on the plant floor alongside cross-functional teams including maintenance, engineering, and operations.
• Strong troubleshooting, documentation, and communication skills. 
• Able to participate in on-call rotation and respond to after-hours production-critical incidents. 

Preferred Qualifications

• Experience with Python scripting for network automation, configuration management, and compliance reporting.
• Experience with cloud connectivity and hybrid network architectures (AWS, Azure, or GCP) in manufacturing contexts.
• Familiarity with MES platforms and their integration with enterprise IT systems. 
• Knowledge of structured cabling standards (TIA-568, IEC 11801) and data center operations within manufacturing environments.
• Experience with SD-WAN solutions for multi-site manufacturing connectivity. 

The Role 

As part of the Embedded Software team, you will help build the critical software foundation that powers the Cerebras Wafer Scale Engine (WSE)—the world’s largest AI processor. Our team owns a diverse range of embedded and system level components that enable the WSE to operate reliably at scale, including microcontroller firmware, wafer level monitoring logic, system administration services, and the Linux platform and BSP layers that keep the entire system running smoothly. 

This role exists at the intersection of embedded systems, platform engineering, and distributed system enablement. As our technology and deployments continue to scale, we are expanding the team with versatile engineers eager to work across multiple layers of the software stack. You will help build administrative services that connect the WSE’s system software to cluster-level orchestration, collaborate closely with hardware and ASIC teams, and contribute to the robustness, visibility, and operability of our next-generation AI systems. 

Responsibilities    

• Develop administrative software that enables communication between system-level software and cluster-level control layers. 
• Provide and extend Linux BSP support, ensuring reliability and maintainability of system level platform components. 
• Collaborate across teams to gather requirements, define scope, plan milestones, and deliver high-quality implementations. 
• Work closely with datacenter operations and debug teams to diagnose system level issues, root cause failures, and implement fixes. 
• Partner with hardware and ASIC teams to design and implement software that monitors system hardware and wafer level behavior. 
• Contribute to improving system reliability, observability, and long-term maintainability across layers of the embedded stack. 
• Participate in code reviews, design discussions, and cross-team technical planning. 

Skills & Qualifications    

Minimum Qualifications 

• Bachelor’s degree in computer engineering, Electrical Engineering, Computer Science, or related field. 

• 5+ years of experience in building production-quality software in C++ or Golang. 

• Solid understanding of embedded systems fundamentals or system hardware interactions. 

• Experience working in cross-functional engineering environments. 

Preferred Qualifications 

• Master’s degree in computer engineering, Electrical Engineering, Computer Science, or related field. 

• Exposure to distributed systems, cluster-level orchestration, or datacenter environments. 

• Familiarity with Linux kernel concepts, device drivers, or BSP layers. 

• Experience debugging hardware/software interactions using tools such as logic analyzers, JTAG, or profiling/tracing frameworks. 

• Experience contributing to system monitoring, observability tooling, or hardware level telemetry pipelines. 

The base salary range for this position is $175,000 to $275,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About the Role:  

We are looking for a hands-on Senior Quality Engineer to drive Manufacturing Quality across our contract manufacturers (CMs) and suppliers. You will be on the front line ensuring that every Cerebras system meets our rigorous quality standards, scales reliably through aggressive production ramps, and ultimately delights our customers. This role will also play a critical part in New Product Introduction (NPI) — setting up control plans, quality gates, and proactive risk mitigation strategies to ensure smooth factory launches.   

As a pacesetter in problem-solving, you will model disciplined quality thinking across the team, raising the bar for structured root cause analysis, corrective actions, and continuous improvement. You will be part of the Quality Engineering team and work closely with Reliability Engineering, Manufacturing Operations, and our CMs to establish a “quality first” environment.  

Responsibilities  

Manufacturing Quality Execution:  

• Serve as the primary quality interface with contract manufacturers; drive alignment on build quality, yield, and corrective actions.  

• Lead day-to-day quality activities at the factory floor — audits, line walks, quality gates, outgoing quality checks.  

• Monitor SPC charts, AOI/inspection data, and parametric performance to proactively identify and correct drift.  

• Coordinate issue containment, corrective action, and verification to closure.  

• Own the quality alert process and partner with Engineering to ensure effective disposition of non-conformances.  

NPI Quality & Control Plan Strategy:  

• Lead NPI quality readiness: establish control plans, process audits, and quality gates for new product builds.  

• Partner with Engineering and Manufacturing to translate product requirements into measurable quality controls.  

• Proactively identify and de-risk potential failure modes before ramp using PFMEA and design reviews.  

• Ensure a smooth handoff from prototype to volume production with stable processes and clear accountability.  

Continuous Improvement & Problem-Solving Leadership:  

• Act as a pacesetter for problem-solving excellence — driving the consistent use of 8D, 5 Whys, and PFMEA across functions.  

• Partner with Reliability Engineering to integrate manufacturing and field data, accelerating detection of systemic issues.  

• Identify and prioritize key quality metrics (yield, defect rates, escape rates, first-pass yield) and ensure visibility to stakeholders.  

• Support manufacturing change reviews to ensure risks are understood and mitigated before implementation.  

Supplier & CM Engagement:  

• Work with Supplier Quality to ensure incoming material quality is stable and consistent with outgoing factory performance.  

• Build strong working relationships with CM quality teams, establishing clear escalation paths and accountability.  

• Participate in supplier audits and quality reviews, feeding learnings back into manufacturing processes.  

Skills and Qualifications:  

• 7–10 years of experience in Manufacturing Quality Engineering for complex electro-mechanical products, ideally in semiconductors, consumer electronics, datacenter hardware, or automotive tech.  

• Proven track record managing CM quality — containment, corrective actions, yield improvement.  

• Strong knowledge of SPC, DOE, control plan strategy, AOI, and inspection systems.  

• Hands-on problem-solving experience with 8D, 5 Whys, PFMEA/DFMEA; known as a go-to problem solver and mentor for others.  

• Experience supporting NPI builds and standing up quality controls at new factories.  

• Data fluency: ability to analyze yield and quality trends in Excel, Python, or SQL and present findings in clear dashboards.  

• Comfortable working in fast-paced environments with aggressive ramps and evolving processes.  

• B.S. in Mechanical, Electrical, Industrial, or Manufacturing Engineering (or related technical field)  

• Strong interpersonal skills with a sense of humor, thriving in an inclusive environment where people feel respected, supported, and motivated to perform at their best.  

The base salary range for this position is $175,000 to $275,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications. 

Role Summary  

Quality, reliability, and uptime are foundational to scaling Cerebras systems. We are seeking an engineer to define and build our prognostics and health monitoring (PHM) capability—developing frameworks to monitor, assess, and predict hardware health across our fleet. 

In this role, you will transform telemetry and operational data into actionable insights and automated responses, enabling early detection of degradation, accurate failure prediction, and proactive actions to keep systems highly available, performant, and resilient. 

This is a highly cross-functional role spanning reliability engineering, data science, and system software, with broad influence across hardware, software, and fleet operations. 

Responsibilities 

• Define the vision, architecture, and roadmap for PHM across deployed systems 

• Design and scale frameworks for health assessment, anomaly detection, and predictive failure modeling 

• Develop and productionize probabilistic models for failure risk, degradation, and remaining useful life 

• Analyze large-scale telemetry, logs, and service data to identify systemic drivers of failures and disruptions 

• Establish health metrics, scoring systems, and fleet-level observability to communicate system risk 

• Partner with system software to integrate monitoring, alerting, and automated mitigation into production 

• Drive closed-loop systems (detection → diagnosis → action → validation) 

• Influence hardware design, qualification, and operations through data-driven insights 

Skills & Qualifications 

Required: 

• Bachelor’s or Master’s in Engineering, Computer Science, Data Science, or related field 

• 8+ years in reliability engineering, data science, fleet analytics, or similar 

• Strong Python and SQL for large-scale data analysis and modeling 

• Experience building and deploying predictive models in production 

• Expertise in applied statistics and probabilistic modeling (e.g., survival analysis, hazard models, Bayesian methods) 

• Experience with large-scale telemetry or distributed system datasets 

• Proven ability to define ambiguous problems and deliver scalable solutions 

Preferred: 

• Experience with HPC systems, AI infrastructure, or datacenter environments 

• Background in PHM, predictive maintenance, or reliability analytics at scale 

• Familiarity with RUL estimation and degradation modeling 

• Understanding of observability systems, telemetry pipelines, and real-time monitoring 

• Background in hardware reliability and failure modes in complex systems 

The base salary range for this position is $150,000 to $250,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role

This role is a high-leverage seat in that build and a deliberate apprenticeship into operating leadership. You will create the business operations, analytics, and execution system that keeps decision-ready insight flowing as the company scales. You will be embedded with operators, turning messy operational reality into durable processes, clear metrics, and repeatable operating rhythms. You will report to the Head of FP&A and work in close partnership with the COO and operations leadership.

Why now

Cerebras is scaling to meet accelerating demand for fast inference. That growth forces rapid expansion across supply chain, manufacturing, and data center deployment. The company needs closed-loop processes and trusted insight assets that scale with the business and remain durable under increasing scrutiny.

Recent market validation, including a marquee partnership with OpenAI, is an early signal of a broader shift: fast inference is becoming foundational, and it is still early days. Operational excellence will compound, and the systems built now will define how efficiently the company scales.

Role at a glance

• Partner with 5 to 10 operational leaders across supply chain, manufacturing, and data center deployment to drive insight and action.
• Own and deliver the right information in the right way at the right time. Build the context that allows the organization to know what happened, the implications, and what to do next.
• Drive closed-loop operational change. Diagnose bottlenecks, redesign processes, and follow through until adoption and measurable improvement are real.
• 50/50 analytics and execution. You build the assets (metrics, dashboards, operating packets) and you drive the behaviors (cadence, accountability, decisions).
• Apprenticeship into operating leadership. We mean it. The hiring manager has used this model repeatedly over the years and can provide references from alumni who have enjoyed meaningful career acceleration.
• Elegant entry point in the cutting edge of AI. If your pace, horsepower, agency, and ambition are elite, this role gives you room to run.. If you make an impact you can chart your own path.
• Small, elite, high-standards team. You are a hands-on leader who learns fast, raises the pace, and may selectively add exceptional talent over time to amplify leverage. This will be a small and mighty team.

What you will build

• Operational analytics infrastructure required to scale supply chain, manufacturing, inventory management, and data center operations with uncompromising quality and speed.
• A decision-quality KPI and reporting architecture: leading indicators, dashboards, recurring reviews, and crisp narratives that operators trust.
• Closed-loop mechanisms that turn operational complexity into repeatable processes: metric definitions, data ownership, reconciliation paths, and accountability loops.
• System integration between operational data sources and finance systems so that decisions are grounded in consistent, auditable definitions.
• Automation and tooling that compounds output, including structured data pulls, workflow automation, and pragmatic use of AI tools to reduce manual work.
• Public-company-ready operating rhythms: clean close-to-insight timelines, documented definitions, and durable operating packets that scale with scrutiny.

What you will own

• Operations partnering: Work shoulder-to-shoulder with operations leadership to translate operational reality into clear priorities, tradeoffs, and actions.
• KPI standards and insight assets: Define the metrics that matter, build the dashboards and operating packets, and keep them accurate as systems and processes evolve.
• Operating cadence: Design and run weekly and monthly performance reviews, ensure decisions are made, and close the loop on follow-through.
• Process change: Identify the highest-leverage process gaps, drive redesign and adoption, and measure impact in throughput, cycle time, quality, and predictability.
• Data quality and reconciliation: Build trust in the numbers by instituting clear definitions, checks, and ownership across operational and finance systems.
• Executive communication: Deliver concise narratives that clearly separate signal from noise and drive action.

What success looks like in the first 6 to 12 months

• You earn trust. Operators proactively pull you into decisions because your work improves outcomes, not just visibility.
• You grow as an operator. You turn consulting-grade problem solving into operating judgment, cross-functional credibility, broader ownership and ultimately IMPACT.
• Durable insight assets are live (dashboards, weekly and monthly operating packets, KPI definitions) with a clear cadence and single-source-of-truth inputs.
• A small number of critical operational processes are redesigned and adopted, with measurable improvements in speed, predictability, and execution quality.
• Cross-functional decisions move faster because the organization shares consistent definitions and a clear view of tradeoffs and constraints.
• Manual reporting load declines materially as automation and self-serve assets replace ad hoc requests and one-off analyses.

What we are looking for

We are hiring for horsepower, motor, agency, and systems thinking. Horsepower means exceptional analytical ability. Motor means a high pace of work. Agency means you identify what the business needs next, build it, and bring others along.

• 6 to 10 years of total experience is a reasonable guide. We will bias toward demonstrated impact and judgment over years.
• 2+ years at a top-tier strategy consulting firm (McKinsey, BCG, Bain, or similar), with readiness to turn generalist problem-solving into company operating impact.
• Experience driving operational change inside a scaling company. This can come from operations, strategy, analytics, or FP&A, as long as you have owned real outcomes.
• High learning velocity. You want an apprenticeship, not just a title, and you learn quickly from direct feedback while owning real outcomes.
• Strong analytical skills and comfort with imperfect data. You can go from ambiguity to a clear framework, then execute.
• High agency. You do not wait for perfect inputs or perfect direction. You move, communicate, and close loops.
• Systems thinker. You build infrastructure that continues working when the pace increases and the data gets messy.
• Technical fluency beyond spreadsheets (preferred). Comfortable with SQL, Python, or adjacent tooling to pull and shape data and automate recurring work.
• Clear executive communication. You distill complexity into concise narratives that drive decisions.
• AI affinity. You proactively apply modern tools to speed up workflows, improve quality, and reduce manual work.

Ways to stand out

• Experience in hardware, semiconductor, manufacturing, supply chain, or data center operations.
• Comfort with Python or similar tooling as evidence of your ability to be a leading creator and user of agents
• A track record of building KPI architectures, operating cadences, and repeatable mechanisms from scratch.
• Experience partnering directly with COO, C-suite, or VP-level operational leaders.
• Demonstrated pattern of high agency: you see problems early and fix them before being asked.

Overview

The Senior Supply Chain Program Manager is responsible for driving data-driven decision-making, process optimization, and strategic initiatives across the supply chain. This role leverages advanced analytics, forecasting, and cross-functional collaboration to enhance efficiency, reduce costs, and improve service levels.

Key Responsibilities

• Cross-functional Collaboration: Partner with engineering, procurement, logistics, and external vendors to align solutions with manufacturing and supply chain objectives.
• Risk Management: Assess supply chain risks (supplier reliability, lead times, geopolitical factors) and develop mitigation strategies.
• Data-driven Decision Making: Leverage manufacturing and supply chain data to measure program impact, optimize processes, and drive continuous improvement.
• Stakeholder Communication: Effectively present program updates to leadership.
• Data Analysis & Reporting: Develop and maintain dashboards, KPIs, and reports to monitor supply chain performance, identify trends, and support strategic decisions.
• Forecasting & Planning: Lead demand and supply planning processes, using statistical models and ERP/MRP systems to optimize inventory levels and reduce stockouts or excess.
• Process Improvement: Identify inefficiencies in procurement, logistics, and inventory management; recommend and implement process improvements.
• Supplier Management:  Ability to collaborate and negotiate favorable outcomes.  
• Supplier Communication:  Ability to communicate effectively at working level and independently with supplier executives.   

Location 

• Sunnyvale, CA

The base salary range for this position is $150,000 to $225,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

Job Summary

We are seeking an experienced System Signal Integrity and Power Integrity Engineer to solve complex, high‑impact integrity challenges in next‑generation AI compute systems. This role is focused on deep technical analysis and hands‑on problem solving across high‑speed interfaces, power delivery networks, rigid and flex interconnects, and advanced packaging.

The ideal candidate is a technical expert engaged to resolve difficult SI/PI problems spanning silicon, package, PCB, flex, and connector domains.

Key Responsibilities

• Solve complex signal integrity and power integrity problems for high‑speed AI compute platforms, including chip‑to‑chip and chip‑to‑board interfaces.
• Perform advanced pre‑layout and post‑layout SI/PI analysis across PCBs, flex circuits, rigid‑flex assemblies, connectors, and advanced packages.
• Lead root‑cause analysis of challenging SI/PI issues such as margin shortfalls, impedance discontinuities, coupling, resonances, and simulation‑to‑hardware mismatches.
• Analyze and resolve SI/PI challenges associated with flex circuits, high‑speed flex connectors, interposers, and advanced packaging technologies.
• Analyze and troubleshoot power delivery networks using DC and AC simulations and hardware correlation to resolve performance and stability issues.
• Define and refine PCB, rigid‑flex, and flex circuit stack‑ups, material selections, and impedance structures as required to meet performance targets.
• Review schematics, PCB layouts, and flex designs to identify SI/PI risks and recommend targeted design changes.
• Work closely with silicon and package design teams to resolve SI/PI issues related to bump/ball assignments, package‑to‑PCB transitions, and interface interactions.
• Act as a technical escalation point for complex SI/PI issues across multiple programs.

Minimum Qualifications

• Master’s degree in Electrical Engineering.
• 10+ years of demonstrated depth of expertise in system-level signal integrity and power integrity engineering for high‑speed hardware systems.

Required Experience and Skills

• Deep expertise in high‑speed serial and parallel interface analysis and debug.
• Strong hands‑on experience with PCB, rigid‑flex, and flex circuit stack‑up design and analysis.
• Advanced SI/PI analysis of flex connectors, high‑density interconnects, and advanced packaging technologies.
• Proficiency with 2D and 3D electromagnetic simulation tools.
• Power delivery network analysis, simulation, and lab correlation at the system level.
• Strong grounding in transmission line theory, microwave engineering, and high‑speed design fundamentals.
• Proven ability to correlate simulation results with hardware behavior and drive concrete design fixes.

Additional Information

Experience with large‑scale AI or high‑performance compute systems is preferred.

The base salary range for this position is $225,000 to $275,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

The Role

Cerebras is seeking a Commissioning Lead to own the end-to-end commissioning and readiness of AI data center infrastructure across colocation environments. This role is responsible for ensuring all systems are tested, validated, and fully operational prior to handover, with zero tolerance for failures in mission-critical environments. You will operate with high ownership in a fast-paced startup environment, driving commissioning execution across multiple concurrent sites and ensuring rapid, reliable capacity bring-up.

Responsibilities

• Lead commissioning strategy and execution across all colo data center deployments.

• Own full lifecycle commissioning from Level 1–5 testing through integrated systems testing (IST).

• Develop and enforce commissioning plans, scripts, and procedures.

• Coordinate with construction, engineering, vendors, and colo providers to ensure readiness.

• Oversee testing of electrical systems (switchgear, UPS, generators), mechanical systems (cooling), and IT infrastructure.

• Ensure all systems meet design intent, performance requirements, and reliability standards.

• Drive issue identification, resolution, and closure prior to handover.

• Manage commissioning agents, vendors, and third-party testing teams.

• Establish standardized commissioning processes for repeatable deployments.

• Track and report commissioning progress, risks, and readiness to executive leadership.

• Ensure all documentation, test results, and turnover packages are complete and accurate.

• Validate base building readiness from colo providers prior to fit-out energization.

• Coordinate integration between landlord systems and tenant infrastructure.

• Ensure alignment on power availability, redundancy, and cooling capacity.

• Resolve interface issues between colo infrastructure and Cerebras systems.

• Hold providers accountable for performance during testing and energization.

Skills & Qualifications

• 10–15+ years of experience in commissioning of mission-critical facilities.

• Deep expertise in data center electrical and mechanical systems.

• Experience leading Level 1–5 commissioning for large-scale projects.

• Strong understanding of high-density compute environments.

• Experience working in colo environments and coordinating landlord/tenant interfaces.

• Proven ability to manage multiple sites and fast-track deployments.

• Strong troubleshooting and problem-solving skills.

• Ability to operate in a fast-paced, high-growth startup environment.

• Excellent communication and stakeholder management skills.

Location: Remote, USA

The base salary range for this position is $220,000 to $260,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

The Role

Cerebras is seeking a Network & Fiber Engineer to lead the design, deployment, and validation of high-performance network and fiber infrastructure across colocation data centers. This role is critical to enabling AI-scale compute clusters by ensuring low-latency, high-throughput connectivity between racks, data halls, and external networks. You will operate with high ownership in a fast-paced startup environment, working closely with construction, commissioning, and IT teams to bring network infrastructure online quickly and reliably.

Responsibilities

• Own end-to-end fiber and network infrastructure deployment across colo data center sites.

• Design fiber pathways, structured cabling systems, and high-density fiber distribution architectures.

• Oversee installation of fiber (SMF/MMF), patch panels, trays, and cable management systems.

• Coordinate with construction and commissioning teams to align network readiness with overall site delivery.

• Validate fiber installations including testing (OTDR, insertion loss, continuity).

• Support deployment of network hardware including switches, routers, and interconnects.

• Ensure low-latency, high-bandwidth connectivity across racks and clusters.

• Develop and maintain standards for fiber design, labeling, and documentation.

• Troubleshoot network and fiber issues during deployment and post-handover.

• Manage vendors, installers, and low-voltage contractors.

• Track progress, risks, and readiness across multiple sites.

• Coordinate with colo providers for meet-me room (MMR) connectivity and cross-connects.

• Ensure alignment on demarcation points and handoff standards.

• Manage external connectivity including ISP, dark fiber, and backbone integration.

• Validate provider fiber infrastructure and resolve interface issues.

Skills & Qualifications

• 7–12+ years of experience in network and/or fiber engineering in data centers or telecom environments.

• Strong experience with fiber design, installation, and testing (OTDR, power meter).

• Familiarity with high-density fiber systems (MPO/MTP).

• Experience deploying and troubleshooting network infrastructure.

• Understanding of data center architectures and high-performance computing environments.

• Experience working in colo environments is highly preferred.

• Ability to manage multiple concurrent deployments.

• Strong problem-solving and troubleshooting skills.

• Excellent communication and coordination abilities.

Location: Remote, USA

The base salary range for this position is $250,000 to $290,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role

The AI conversation moves fast — new models ship weekly, benchmarks shift overnight, and the community's attention resets constantly. Cerebras has a massive speed advantage in inference, and this role exists to make sure that advantage is visible, understood, and top-of-mind wherever developers and AI builders are paying attention. 

As Senior Product Marketing Manager, you'll own realtime product marketing for Cerebras inference. You'll create high-impact technical content — blog posts, benchmark analyses, social threads — that positions Cerebras at the center of the AI conversation. You'll find edge in benchmarks, develop new demos that showcase what speed unlocks, and build influencer and community programs that scale our reach beyond what we can do alone. You'll set editorial direction with a high degree of autonomy, reading the market daily and moving as fast as it does. This is a role for someone who lives in the AI ecosystem, uses the tools every day, and knows how to turn a speed advantage into a marketing advantage. 

What You'll Own 

Realtime Product Marketing 

• Identify and position Cerebras' edge in a rapidly shifting competitive landscape — identify what matters, what's changing, and where we win 
• Insert Cerebras into the AI conversation. Create short-form and long-form content that highlights Cerebras' advantage in relation to the most important online conversation around AI (eg. Agents, OpenClaw etc.) 

Community & Influencer Marketing 

• Build programs that generate grassroots community marketing and organic endorsement of Cerebras — through content creators, influencers, and popular software communities 
• Feature Cerebras in leaderboards and third-party products to showcase our unique product capabilities and leadership position 

Marketing Programs & Organic Growth 

• Develop new and original angles to market both Cerebras capabilities and the success of customers building on our inference 
• Develop new formats and campaigns that breakthrough in a noisy market and keep Cerebras top-of-mind with technical audiences 

Skills And Qualifications   

• 5+ years of product marketing experience in AI, ML infrastructure, or developer tools with a strong portfolio of published technical content 
• Deep fluency in AI coding models and agentic coding — you understand how these tools work, how developers evaluate them, and what intelligence and speed mean in practice 
• Hands-on experience benchmarking AI models and producing benchmark content that resonates with technical audiences 
• Native user of AI coding tools — you use them daily and can create technical artifacts, run evaluations, and build demos independently 
• Experience building and scaling influencer and content creator programs that drove measurable organic reach 
• Track record of creating and scaling organic content programs — both first-party and through external contributors 
• Strong technical writing and editorial judgment — you can tell the story of why speed matters and make it stick 
• Self-directed and autonomous — you identify what needs to exist, build it, and ship it without waiting for a brief 

The Role

Cerebras is seeking a Director of Procurement to lead sourcing, contracting, and supply chain execution for data center infrastructure and critical equipment. This role is responsible for securing capacity, negotiating commercial terms, and ensuring timely delivery of long-lead equipment to support rapid AI infrastructure deployment.

You will operate with high ownership in a fast-paced startup environment, driving both strategic supplier partnerships and tactical execution across multiple concurrent builds.

Responsibilities  

• Own procurement strategy for all data center equipment including electrical, mechanical, and IT infrastructure.
  • Lead sourcing and contracting for major equipment packages (switchgear, transformers, UPS, generators, cooling systems, racks).
  • Negotiate commercial terms, pricing, lead times, and risk allocation with suppliers and contractors.
  • Develop and manage master service agreements (MSAs), purchase agreements, and vendor contracts.
  • Drive cost optimization while maintaining speed and quality of delivery.
  • Partner with construction and engineering teams to align procurement with deployment schedules.
  • Manage supplier performance, ensuring on-time delivery and quality compliance.
  • Identify and mitigate supply chain risks, including long-lead constraints and market volatility.
  • Build strategic relationships with key OEMs and vendors.
  • Establish scalable procurement processes, tools, and reporting mechanisms.
  • Track and forecast spend across multiple projects and regions.
• Lead contract negotiations for EPCs, GCs, and major vendors.
  • Define contract structures that align incentives with schedule and performance outcomes.
  • Manage change orders, claims, and commercial disputes.
  • Ensure clear scope definition and risk allocation across all agreements.
  • Standardize contract templates for rapid deployment across multiple sites.
  • Partner with legal and finance to ensure compliance and governance.

Skills & Qualifications  

• 12–15+ years of experience in procurement, sourcing, or supply chain for large-scale infrastructure projects.
  • Strong experience in data center, mission-critical, or industrial environments.
  • Deep knowledge of electrical and mechanical equipment supply chains.
  • Proven ability to negotiate complex, high-value contracts.
  • Experience managing global suppliers and multi-site programs.
  • Strong financial acumen and cost management expertise.
  • Ability to operate in a fast-moving, ambiguous startup environment.
  • Excellent stakeholder management and communication skills.

Location: Remote, USA

The base salary range for this position is $280,000 to $350,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role

The Manager will be primarily responsible for tracking and recovery of Cerebras’s data center infrastructure and assets globally through asset end of life. This leadership position requires an organized, highly motivated professional with the ability to drive operational and process improvements. The individual will perform a variety of tasks ranging from routine to complex analysis and play a critical part in asset tracking operations, including asset dispositions, transfers, and periodic cycle counts.

This role requires comfort operating in a fast-paced, evolving environment, where priorities may shift and processes are still being built. The successful candidate will be expected to ramp quickly, close gaps, and contribute immediately, bringing structure, judgment, and execution while helping where needed across the broader finance organization.

You will partner closely with Manufacturing, Supply Chain, FP&A, Deployment, and Engineering to establish scalable costing models, strengthen controls, and support external reporting readiness as the company prepares to operate as a public company.

Responsibilities

Business Process Optimization

• Partner with Supply Chain operations to establish best-in-class asset tracking policies, procedures, and internal controls.
• Develop expertise and thoroughly understand the features, functionality, and capability of the fixed asset ERP software system and serve as a thought partner to Finance leadership on process redesign
• Drive projects effectively in a high-growth, fast-paced environment, balancing strategic leadership with hands-on execution as the business scales.

Data Center asset management and accounting

• Own accounting and cost modeling for data center fixed assets, including AI compute systems, servers, networking, power, and cooling infrastructure.
• Support processes to ensure all fixed assets are physically or systematically verified and aligned with the Company’s data center infrastructure management system and asset tracking systems
• Establish capitalization policies, useful lives, depreciation methods, and impairment assessments.
• Lead and coordinate periodic physical inventory and ensure the system records are updated accordingly
• Assist with developing and documenting asset accounting processes including asset transfer, cycle count, and disposition procedures in addition to developing and monitoring system controls and procedures
• Partner with Infrastructure and FP&A to forecast capital spend and allocation of depreciation expense.

Data Center Lease accounting

• Own end-to-end lease accounting under ASC 842, including lease identification, classification, initial measurement, and ongoing remeasurement/modifications.
• Manage the lease close process: prepare/review journal entries, reconciliations, rollforwards, and support monthly/quarterly reporting and variance analysis.
• Maintain the lease system of record (e.g., LeaseQuery,); ensure data integrity, controls, and timely updates for new leases, amendments, renewals, and terminations.
• Partner with Legal, Procurement, and FP&A to review lease terms, assess embedded leases, and ensure accounting conclusions are documented.
• Support lease-related disclosures and support external audit/SOX compliance: process documentation, control design/testing, and audit request management.
• Drive process improvements and standardization (templates, checklists, policy updates) to scale lease accounting as the portfolio grows.

Compliance, Reporting & IPO Readiness

• Ensure compliance with GAAP, SOX, and internal policies.
• Support SEC reporting and disclosures related to fixed assets.
• Assist with ad-hoc analyses and cross-functional initiatives as needed to support business priorities.

Skills And Qualifications   

• Bachelor’s degree in Accounting, Finance, or related field (Master’s preferred).
• CPA or CMA strongly preferred.
• 5+ years of combined experience at a Big 4 accounting firm and manufacturing, hardware, or infrastructure-intensive environments
• Strong knowledge of GAAP, costing methodologies, and fixed assets policies and procedures.
• Demonstrated experience automating processes and activities, including data center asset management
• Strong Microsoft Office skills (Excel/PowerPoint/Word/Outlook) required
• Experience with expense allocation models and capital-intensive cost structures.
• Exposure to asset tracking solutions along with experience ERPs such as Oracle and NetSuite

Personal Attributes

• Thrives in fast-paced, high growth, high-ambiguity environments.
• Ability to work with high volumes of unstructured data and create appropriate data structures to provide insights
• Able to ramp quickly, identify gaps, and take ownership.
• Hands-on, detail-oriented, and execution-focused.
• Excellent organizational and time management skills with the ability to multi-task
• Strong cross-functional communicator with sound judgment.

The Role 

We are seeking a highly skilled and motivated Manufacturing Bring-up Engineer to join our team. As the Manufacturing Bring-up Engineer you will support our system level bring-up process execution, implementation, and evolution in the manufacturing pipeline. This is a high visibility role that requires strong technical expertise, coordination, and collaboration to deliver our product from manufacturing to the customer. 

Responsibilities 

• Support the Cerebras manufacturing bring-up process execution to configure, test, and validate system performance prior to customer shipment 

• Collaborate cross-functionally with Asic, SW, Diagnostics, and QA teams to further automate and streamline the workflow for optimal manufacturing efficiency 

• Troubleshoot and resolve technical issues during system bring-up across Asic, SW, and QA domains 

• Design and implement efficient processes to manage and track system bring-up status and progress 

• Track and report on critical bring-up metrics to drive continuous improvement  

• Implement further SW automation and efficiencies to effectively scale the manufacturing bring-up process in support of the manufacturing roadmap 

Skills & Qualifications 

• BS or MS in EE, ECE, CS or equivalent work experience 

• 3+ years of industry experience in an operations environment 

• Experience in hardware bring-up and the debug of complex systems 

• Working knowledge and experience in Asic bringup and test processes 

• Working knowledge of scripting in languages such as Python and/or Perl 

• Proven experience in system bring-up and validation of complex computer systems or equivalent technologies 

• Understanding of computer system architecture and hardware components  

• Proficiency in scripting and automation tools for system bringup  

• Excellent problem-solving and communication skills with the ability to work collaboratively in a fast-paced environment 

• Very strong coordination and collaboration skills to manage a business-critical workflow directly in support of customer demand 

Preferred: 

• Familiarity in creating test and s/w infrastructure at large scale 

• Working across global time zones  

Location 

Bangalore, India/Toronto, Canada/ Sunnyvale, California.

The base salary range for this position is $170,000 to $230,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

The Role 

We are seeking a highly skilled and motivated Manufacturing Bring-up Engineer to join our team. As the Manufacturing Bring-up Engineer you will support our system level bring-up process execution, implementation, and evolution in the manufacturing pipeline. This is a high visibility role that requires strong technical expertise, coordination, and collaboration to deliver our product from manufacturing to the customer. 

Responsibilities 

• Support the Cerebras manufacturing bring-up process execution to configure, test, and validate system performance prior to customer shipment 

• Collaborate cross-functionally with Asic, SW, Diagnostics, and QA teams to further automate and streamline the workflow for optimal manufacturing efficiency 

• Troubleshoot and resolve technical issues during system bring-up across Asic, SW, and QA domains 

• Design and implement efficient processes to manage and track system bring-up status and progress 

• Track and report on critical bring-up metrics to drive continuous improvement  

• Implement further SW automation and efficiencies to effectively scale the manufacturing bring-up process in support of the manufacturing roadmap 

Skills & Qualifications 

• BS or MS in EE, ECE, CS or equivalent work experience 

• 3+ years of industry experience in an operations environment 

• Experience in hardware bring-up and the debug of complex systems 

• Working knowledge and experience in Asic bringup and test processes 

• Working knowledge of scripting in languages such as Python and/or Perl 

• Proven experience in system bring-up and validation of complex computer systems or equivalent technologies 

• Understanding of computer system architecture and hardware components  

• Proficiency in scripting and automation tools for system bringup  

• Excellent problem-solving and communication skills with the ability to work collaboratively in a fast-paced environment 

• Very strong coordination and collaboration skills to manage a business-critical workflow directly in support of customer demand 

Preferred: 

• Familiarity in creating test and s/w infrastructure at large scale 

• Working across global time zones  

Location 

Sunnyvale, California/ Bangalore, India/Toronto, Canada.

The base salary range for this position is $170,000 to $230,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role

As a Test Development Engineer on our manufacturing team you will be working with diagnostics, system design, manufacturing, and quality teams to develop test automation solutions for our products from PCBA to system level. You will also work closely with our contract manufacturing sites to fulfill a complete test automation solution for manufacturing test data, yield improvement, and traceability.

Responsibilities

• Develop and design manufacturing test automation software/scripts to test Cerebras products from PCBA to system level.
• Develop and implement GUI solutions for test automation.
• Work with our contract manufacturers to develop and implement a test data reporting portal for manufacturing traceability and analysis.
• Sustain our current test software and infrastructure and help root cause and resolve any manufacturing test software issues or hardware defects.
• Design a web interface for user to modify/edit settings from mySQL database on AWS.
• Setup the various infrastructures at our manufacturing sites to support test equipment and server operation.
• Interact with contract manufacturing site for all the technical issues relating to manufacturing test.
• Work with diagnostics, system design, manufacturing and quality team to bring up test automation suites for the new products.

Requirements

• Bachelors in computer science, electrical engineering, or other related field.
• 5+ years of experience in test automation, test development or related experience.
• Skilled in C/C++, Visual Studio, Python programming languages.
• Good knowledge of js, MySQL, SQL, SQL Server Reporting Service.
• Good knowledge of Pexpect, SSH, Telnet, RS-232, bash script.
• Good knowledge of Windows, Linux, Ubuntu, Centos, VNC viewer, Console server.
• Debugging skills and knowledge of debugging complex software stack.

Preferred Skills

• Experience in GUI development.
• Experience in Web development.
• Experience in API development.

About The Role 

We are seeking an experienced Manufacturing Test Engineering Lead to lead our team of manufacturing test and test automation engineers. The successful candidate will be responsible for overseeing the development, implementation, and maintenance of test strategies, processes, and systems to ensure the quality and reliability of our products. This is a key leadership role that requires strong technical expertise, excellent communication skills, and the ability to motivate and manage a team of engineers. 

Key Responsibilities: 

• Lead and manage a team of manufacturing test and test automation engineers, providing guidance, coaching, and development opportunities to ensure their growth and success. 
• Develop and implement comprehensive test strategies and plans to ensure product quality and reliability. 
• Collaborate with cross-functional teams, including design engineering, manufacturing, and quality assurance, to ensure test requirements are met. 
• Lead the team to develop and implement test systems and processes for efficiency, reduce costs, and enhance product quality improvements. 
• Collaborate with test automation and diagnostics team to design, develop, and deploy automated test solutions. 
• Identify areas for process improvement and implement changes to optimize test efficiency, reduce cycle time, and improve product quality. 
• Develop and track key performance indicators (KPIs) to measure test process effectiveness and efficiency. 
• Provide technical guidance and expertise to the test engineering team, including troubleshooting and resolving complex test-related issues. 
• Develop and manage budgets for test engineering activities, including capital expenditures and operating expenses. 
• Ensure effective utilization of resources, including personnel, equipment, and facilities. 
• Communicate test plans, results, and issues to stakeholders, including management, design engineering, manufacturing, and quality assurance. 
• Collaborate with other departments to ensure alignment and effective implementation of test strategies and processes.  

Requirements: 

• Bachelor's degree in Electrical Engineering, Computer Engineering, or a related field. 
• 8+ years of experience in manufacturing test engineering 
• Proven track record of developing and implementing effective test strategies and processes for a manufacturing environment. 
• Strong knowledge of test engineering principles, including test development, test automation, and test process improvement. 
• Familiarity with industry-standard test equipment and software, such as National Instruments, Agilent, or LabVIEW. 
• Experience with automated test frameworks and programming languages, such as Python, C++, or Java. 
• Excellent communication, leadership, and interpersonal skills. 
• Ability to motivate and manage a team of engineers, providing guidance, coaching, and development opportunities. 
• Strong problem-solving and analytical skills, with the ability to troubleshoot complex test-related issues. 

The base salary range for this position is $175,000 to $275,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role 

As a Research Engineer on the Inference ML team at Cerebras Systems, you will adapt today's most advanced language and vision models to run efficiently on our flagship Cerebras architecture. You'll work alongside ML researchers and engineers to design, prototype, validate, and optimize models, gaining end-to-end exposure to cutting-edge inference research on the world's fastest AI accelerator. 

You will focus on pushing the frontier of speculative decoding, large-model pruning and compression, sparse attention, and sparsity-driven techniques to deliver low-latency, high-throughput inference at scale. 

Responsibilities 

• Implement and adapt transformer-based models (NLP and/or vision) to run on Cerebras hardware
• Assist in optimizing models for inference performance (latency, throughput)
• Run experiments, analyze results, and support model improvements
• Help bring up and validate models on the Cerebras system
• Debug and troubleshoot model or system issues with guidance from senior team members
• Support profiling and performance analysis using internal tools
• Collaborate with cross-functional teams (ML, software, hardware) on model integration

Minimum Qualifications 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• 1–3 years of experience in software engineering or machine learning in a similar capacity (internships count)
• Experience with Python and at least one ML framework (e.g., PyTorch, Transformers, vLLM or SGLang)
• Understanding of deep learning concepts (e.g., neural networks, transformers)
• Experience with Generative AI and Machine Learning systems
• Strong programming skills in Python and/or C++

Preferred Qualifications 

• Experience with speculative decoding, neural network pruning and compression, sparse attention, quantization, sparsity, post-training techniques, and inference-focused evaluations. 
• Exposure to large language models or computer vision models
• Experience running experiments or tuning models
• Familiarity with tools like PyTorch, Hugging Face Transformers, or similar
• Basic understanding of performance concepts (e.g., latency, throughput)
• Experience working in Linux environments

Responsibilities:

• Lead the design and implementation of system-level debugging, validation, and observability platforms.
• Develop automated systems for collecting and analyzing numerical, and execution anomalies.
• Create visualization and analysis tools to enable efficient root-cause investigation.
• Build frameworks for failure classification, regression detection, and anomaly monitoring.
• Extend compilers, runtimes, and programming interfaces to support advanced profiling and instrumentation.
• Improve system bring-up, low-level debug, and validation workflows.
• Partner cross-functionally with compiler, hardware, firmware, runtime, and infrastructure teams.
• Establish best practices for debuggability, reliability, and operational excellence.
• Lead high-impact initiatives.
• Support incident response and drive long-term corrective actions.

Qualifications: 

• Strong proficiency in C++ and Python, with a track record of building reliable, high-performance systems and tooling.
• Demonstrated experience debugging complex hardware/software systems and driving issues to root cause.
• Experience analyzing system-level data structures, execution graphs, or dependency networks for diagnostics and validation.
• Proven ability to design and build intuitive visualization and analysis tools for complex technical data.

• Experience with compiler internals, custom hardware interfaces, or low-level protocol design.

• Strong written and verbal communication skills, with the ability to explain technical concepts to diverse stakeholders.
• Ability to work independently and lead complex technical projects end-to-end.

Preferred Skills & Qualifications

• Familiarity with machine learning training and inference pipelines, especially distributed training and large-model scaling.
• Prior work on high-performance clusters, HPC systems, or custom hardware/software co-design.

About The Role

Engineers on the inference performance team operate at the intersection of hardware and software, driving end-to-end model inference speed and throughput. Their work spans low-level kernel performance debugging and optimization, system-level performance analysis, performance modeling and estimation, and the development of tooling for performance projection and diagnostics.

Responsibilities

• Build performance models (kernel-level, end-to-end) to estimate the performance of state of the art and customer ML models.
• Optimize and debug our kernel micro code and compiler algorithms to elevate ML model inference speed, throughput and compute utilization on the Cerebras WSE.
• Debug and understand runtime performance on the system and cluster.
• Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.

Requirements

• Bachelors / Masters / PhD in Electrical Engineering or Computer Science.
• Strong background in computer architecture.
• Exposure to and understanding of low-level deep learning / LLM math.
• Strong analytical and problem-solving mindset.
• 3+ years of experience in a relevant domain (Computer Architecture, CPU/GPU Performance, Kernel Optimization, HPC).
• Experience working on CPU/GPU simulators.
• Exposure to performance profiling and debug on any system pipeline.
• Comfort with C++ and Python.

About The Role

As a Network Architect on the Cluster Architecture Team, you will work closely with the vendors, internal networking teams and industry peers to develop best-in-class front-end datacenter and interconnect architecture of the current and future generations of the Cerebras AI clusters. You will be responsible for developing proof-of-concept of new network designs and features enabling resilient and reliable network for AI workloads. The role will require cross-functional collaboration and interaction with diverse hardware components (e.g., network devices and the Wafer-Scale Engine) as well as software at several layers of the stack, from host-side networking to cluster-level coordination. The role also requires understanding of network monitoring systems and network debugging methodologies.

Responsibilities

• Design and architect front-end network fabrics for AI/ML and HPC systems.
• Identify and resolve performance and efficiency bottlenecks, ensuring high resource utilization, low latency, and high-throughput communication.
• Lead cross-functional technical projects spanning multiple teams and integrating diverse software and hardware components to deliver advanced networking technologies.
• Foster clear and effective communication across teams and stakeholders.
• Collaborate with vendors and industry partners to shape network hardware and feature roadmaps.
• Represent Cerebras in industry forums and technical communities.
• Serve as the central point of contact for network reliability issues.

Skills & Qualifications

• Ph.D. in Computer Science or Electrical Engineering + 10 years industry experience or Master’s in CS or EE + 15 years industry experience.
• 8+ Years of experience in large scale network designs in datacenter and cloud environments.
• Extensive experience debugging networking issues in large distributed systems environment with multiple networking platforms and protocols.
• Experience of managing and leading multi-phase and multi-team projects.
• Networking platforms like Juniper, Arista, Cisco, Open box architectures (Sonic, FOBSS).
• Networking protocols like VXLAN, EVPN, RoCE, BGP, DCQCN, PFC, Streaming telemetry.
• Familiarity with automation languages like Python, or Go.
• Familiarity with Network visibility and management systems.
• Prior experience in hyperscalers or cloud service providers is strongly preferred.

• Focus on CPU and memory subsystem optimizations for our Runtime software driver, enabling faster key cloud and ML training/inference workloads across modern x86 machines that form the backbone of our AI accelerator.
• Develop and enhance algorithms for efficient data movement, local data processing, job submission, and synchronization between various software and hardware components.
• Optimize our workloads using advanced CPU features like AVX instructions, prefetch mechanisms, and cache optimization techniques.
• Perform performance profiling and characterization using tools such as AMD uprof, and reduce OS level overheads.
• Influence the design of Cerebras' next-generation AI architectures and software stack by analyzing the integration of advanced CPU features and their impact on system performance and computational efficiency.
• Engage directly with the AI and ML developer community to understand their needs and solve contemporary challenges with innovative solutions.
• Collaborate with multiple teams within Cerebras, including architecture, research, and product management, to elevate our computational platform and influence future designs.

• BS, MS, or PhD in Computer Science, Computer Engineering, or a related field.
• 5+ years of relevant experience in performance engineering, particularly in optimizing algorithms and software design.
• Strong proficiency in C/C++ and familiarity with Python or other scripting languages.
• Demonstrated experience with memory subsystem optimizations and system-level performance tuning.
• Experience with distributed systems is highly desirable, as it is crucial to optimizing the performance of our Runtime software across multiple x86 hosts.
• Familiarity with compiler technologies (e.g., LLVM, MLIR) and with PyTorch and other ML frameworks.

• Develop design specifications for new machine learning and linear algebra kernels and mapping to the Cerebras WSE System using various parallel programming algorithms.
• Develop and debug kernel library of highly optimized low level assembly instruction and C-like domain specific language routines to implement algorithms targeting the Cerebras hardware system.
• Develop and debug high-performance kernel routines in low-level assembly and a custom C-like (CSL) language, implementing algorithms optimized for the Cerebras hardware system.
• Using mathematical models and analysis to measure the software performance and inform design decisions.
• Develop and integrate unit and system testing methodologies to verify correct functionality and performance of kernel libraries.
• Study emerging trends in Machine Learning applications and help evolve Kernel library architecture to address computational challenges of the start-of-the-art Neural Networks.
• Interact with chip and system architects to optimize instruction sets, microarchitecture, and IO of next generation systems.

• Bachelor’s, Master’s, PhD or foreign equivalents in Computer Science, Computer Engineering, Mathematics, or related fields.
• Understanding of hardware architecture concepts — must be comfortable learning the details of a new hardware architecture.
• Skilled in C++ and Python programming languages.
• Good knowledge of library and/or API development best practices.
• Strong debugging skills and knowledge of debugging complex software stack.

• Experience in kernel development and/or testing.
• Familiarity with parallel algorithms and distributed memory systems.
• Experience in programming accelerators such as GPUs and FPGAs.
• Familiarity with Machine Learning neural networks and frameworks such as TensorFlow and PyTorch.
• Familiarity with HPC kernels and their optimization.

• Characterize and enhance the performance and reliability of advanced ML hardware/software systems, with emphasis on reducing power and thermal fluctuations.
• Analyze ML workloads, software kernels, and hardware architecture for power and performance impacts, and synthesize high-level insights across these layers.
• Develop creative software solutions to improve reliability and performance, collaborating cross-functionally to deploy these solutions in production.
• Influence the design of Cerebras' next-generation AI architecture and software stack through rigorous workload analysis and computational efficiency optimization.
• Partner with ML engineers, researchers, and reliability specialists to understand model behavior and drive system-level improvements from a software perspective.
• Collaborate with teams in architecture, silicon, and research to advance our computational platforms and influence future system designs.

• BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field.
• 3+ years of relevant experience in performance engineering, reliability, computer architecture, and/or software design.
• Proficiency in Python or other scripting languages.
• Experience with C/C++ and assembly programming.
• Demonstrated expertise with system-level performance and reliability optimization.
• Strong verbal and written communication skills.
• Nice to have: Hands-on experience with ML models, ML frameworks, and collective communication.
• Nice to have: Understanding of thermal management principles and power delivery for advanced semiconductors.

In late 2024, we launched Cerebras Inference, the fastest Generative AI inference service in the world, over 10 times faster than GPU-based hyperscale cloud inference. Since launch, we’ve scaled to meet the surging demand from AI labs, enterprises, and a thriving developer community. 

In October 2025, we announced our series G funding, raising $1.1 billion USD to accelerate the expansion of our products and services to meet global AI demand. 

About the team 

The Cerebras Inference team’s mission is to deliver the world’s most performant, secure, and reliable enterprise-grade AI service. We build and operate large-scale distributed systems that power AI inference at unprecedented speed and efficiency. Join us to help scale inference and accelerate AI. 

About the role 

We’re looking for a hands-on Reliability Tech Lead (IC) to own the mission of making Cerebras Inference the most reliable AI service in the world. You will drive reliability strategy and execution across our inference stack, from client SDKs and public-cloud multi-region deployments to wafer-scale systems in specialized data centers. 

In this role, you will define SLOs and incident-response frameworks, design and implement reliability mechanisms at scale, and partner across hundreds of engineers to ensure our service meets world-class reliability standards. 

If you are passionate about building and operating massive-scale, low-latency, high-reliability distributed systems, we want to hear from you. 

Responsibilities: 

• Define and drive reliability strategy: establish SLOs and ensure alignment across engineering. 

• Design and implement reliability mechanisms: build and evolve systems for fault detection, graceful degradation, failover, throttling, and recovery across multiple regions and data centers. 

• Lead large-scale incident management: own postmortems, root-cause analysis, and prevention loops for reliability-related incidents. 

• Architect for reliability and observability: influence system design for redundancy, durability, and debuggability. 

• Develop reliability tooling: create internal tools and frameworks for chaos testing, load simulation, and distributed fault injection. 

• Collaborate broadly: work across software, infrastructure, and hardware teams to ensure reliability is embedded into every layer of our inference service. 

• Monitor and communicate reliability metrics: build dashboards and alerts that measure service health and provide actionable insights. 

• Mentor and influence: guide engineers and set best practices for designing, testing, and operating reliable large-scale systems. 

Skills & Qualifications: 

• Bachelor's or master's degree in computer science or related field. 

• 7+ years of experience in backend, infrastructure, or reliability engineering for large-scale distributed systems. 

• Strong programming skills in at least one popular backend programming language such as Python, C++, Go, or Rust. 

• Deep and hard-earned experience of reliability principles: SLO/SLI/SLA design, incident response, and postmortem culture. 

• Excellent communication and cross-functional leadership skills. 

• Bonus: prior experience building large-scale AI infrastructure systems. 

Location: Sunnyvale 

We're hiring a Principal Engineer for our Inference Cloud Platform. This team owns the cloud layer behind our Inference Service, including availability, latency, reliability, and multi-region scale. 

This is one of the most senior IC roles on the team, for someone who can identify the highest-leverage platform problems, set direction across multiple teams, define long-term architecture, and write production code on critical paths. 

Many of the key decisions are ambiguous at the outset; you’ll need to frame the problem, make tradeoffs, and drive execution without a clear spec. 

The scope includes multi-region traffic architecture, graceful degradation under bursty AI workloads, high-QPS performance, and the operating model for a platform that needs to remain fast and available under changing demand. You'll partner closely with ML, Product and Infrastructure teams. 

Responsibilities 

• Problem Definition & Prioritization. Identify the most important technical problems for the platform, often before there's a clear ask. Make explicit tradeoff decisions about what the platform will and won't support, with reasoning that holds up under scrutiny from senior engineering leadership. 

• Platform Direction. Set the long-term technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time. 

• Reliability & Performance. Architect active-active systems with rapid failover and graceful degradation (circuit breaking, backpressure, load shedding) with clear SLOs. Drive improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand. 

• Code & Design Reviews. Contribute production code in critical paths, review designs and implementations, and make architectural decisions including build-vs-buy tradeoffs with long-term operational consequences. 

• Production Leadership. Lead on the hardest production issues and cross-system bottlenecks. Drive observability, incident response, capacity planning, and post-incident improvement with a high standard for operational rigor. 

• Technical Strategy Beyond Your Team. Drive platform-wide decisions across adjacent teams on reliability, API design, capacity planning, and deployment strategy through strong technical judgment. Translate product and business requirements into scalable system designs and drive alignment on shared infrastructure decisions. 

• Mentorship. Raise the quality of technical decision-making across teams through design feedback, pairing, and clear engineering standards. 

Skills & Qualifications 

• 10+ years of experience in software engineering, with substantial individual contributor experience building and operating large-scale distributed systems or cloud infrastructure.  

• Deep expertise in distributed systems architecture in cloud environments, including networking, compute orchestration, container platforms, and multi-region production services. 

• Strong track record of making sound architectural decisions for highly available, latency-sensitive systems at scale, demonstrated through systems you built directly. 

• Experience optimizing latency, throughput, and efficiency in high-QPS systems. Experience with TTFT and tail-latency reduction is a strong plus. 

• Strong proficiency in backend or systems languages such as Go, C++, or Python, with the expectation that you can contribute production code directly. 

• Experience designing observability and reliability practices, including metrics, logging, tracing, alerting, incident response, and SLI/SLO/SLA-driven operations. 

• Ability to influence senior engineers, technical leads, and cross-functional partners through technical credibility, communication, and judgment. 

• Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads is a plus. 

About The Role

Cerebras is adding an ML team that can focus on a new ML effort that can align with existing teams. We are seeking a principal investigator who will partner with our ML leaders to formulate the new effort and to build up the new team and capabilities. This new team would coordinate with our current ML teams: Field ML, which works directly with customers, Applied ML, which builds new ML capabilities and applications for customers, and Core ML, which adapts ML algorithms to find unique capabilities of Cerebras hardware. The new team could take up the same or complementary responsibilities.

We would like the new team to work on some of the following areas:

• Post-training and reinforcement learning: Techniques used to improve model deployment quality through further training, tuning, RL, and focus on particular downstream tasks;
• Dataset curation and optimization: Techniques to collect and select high-quality data, which can help models to train or tune more quickly or to higher quality;
• LLM Pretraining: Techniques to ensure stability and compute-efficiency while pretraining high quality models. May include training dynamics, parameterizations, numerics, or others;
• Sparsity: Techniques to sparsify models or data that improve training time-to-quality, or optimize inference speed or throughput;
• Domains: Coding agents, reasoning agents, generative language, image, video.

Principal Investigator Responsibilities

• Build up a team capable of industry research and advanced development.
• Organize various advanced development topics into cohesive agenda.
• Adapt novel algorithms and model architectures to run on the Cerebras platform.
• Systematically train, tune, and evaluate models to guide/advise production scenarios.
• Collaborate with other teams to co-design next-generation hardware and software architectures.
• Collaborate with external partners (customers, academic) to drive insight and credibility.

Skills & Qualifications

• PhD in Computer Science or related field.
• Strong grasp of ML theory in one or more of the above areas.
• Proven experience engineering ML systems for scale or production deployment.
• Experience leading a team of researchers or engineers.

Preferred Skills & Qualifications

• Track record of patents or publications in top-tier conferences or journals.
• Experience with large language models (e.g., GPT family, Llama).
• Experience with distributed training concepts and frameworks.
• Experience in training speed optimizations, such as model architecture transformations to target hardware, or low-level kernel development (e.g., Triton).
• Ability to analytically model or optimize system performance.

Our customers span leading AI Native companies, Fortune 500 Enterprises, Sovereign AI and Federal programs, and leading research institutions. Our mission is to deliver the platform that unlocks the next generation of AI applications, providing the fundamentally new capability to leverage the most intelligent models at real-time serving speeds. 

Why Cerebras?  

Here at Cerebras, we have built the world’s first wafer-scale compute platform and software stack, purpose-designed to accelerate generative AI by over 10-20x what is possible on legacy processors today. AI developers are limited today by the constant tradeoffs between model quality, speed, and cost, and Cerebras’ mission is to remove these limitations to unlock AI creativity and potential.  

• Unmatched speed. Our third-generation Wafer-Scale Engine (WSE-3) delivers sub-ms inference latencies and training throughput that eclipses GPU clusters by over 10x. Think instant code generation, instant design creation, agents that interact seamlessly and responsively to their users and environment.  
• Full-stack innovation. From custom silicon to compilers, model research, and turnkey cloud inference, we are innovating and integrating at every layer so customers can focus on breakthroughs, not bottlenecks. 
• Real-world impact. Cerebras customers are transforming industries across healthcare, energy, science, government, startup ecosystems, and more. We’re proud to be serving customers spanning the Fortune 500, government labs, and AI-native unicorns. 
• Backed to win. Cerebras is supported by top investors, like Benchmark, Altimeter, Eclipse, and Coatue. 
• Fearless and fun culture. We’re a close-knit, creative team that tackles all challenges with optimism and collaboration. We’ve already productized the world’s largest chip by over 50x. How hard can the next problem be? :)   

About The Role 

As a founding member of the Strategic Verticals product team at Cerebras, you are the tip of the spear for our company. You’ll embed with our most strategic customers, from AI-native startups shipping 0-to-1 products to Fortune 500 enterprises transforming their industries, to translate and guide their ambitions into blazing-fast, production-ready AI solutions.  

Think of yourself as part product leader, part technical expert, and part GTM strategist:  

1. Own the outcome – From first whiteboard session to scaled deployment, you are directly accountable for customer success, adoption, and expansion. 
2. Design for speed – Craft PoCs that showcase Cerebras’ latency super-powers, advise on model selection / fine-tuning, and benchmark end-to-end performance. 
3. Navigate complexities – pitch new ideas, align internal and customer stakeholders, unblock hurdles, and convert customer interest into long-term, thriving partnerships. 
4. Shape the roadmap – Distill customer insights into structured product feedback requirements, influencing future software features and chip and cluster designs.  

Successful candidates will be passionate about creative problem solving and idea generation, learning and embedding into new domains, building relationships, and delighting customers.  

You’ll have the opportunity to learn about and enable some of the most impactful AI products in the world, with industry-leading organizations across each vertical. You will get to work closely with a tight-knit product team, in a fast-moving but supportive environment. Your scope and career here will be driven by your passion, ability, and impact – not by your seniority or prior experience. 

Key Responsibilities 

You will: 

• Be the product leader on our most critical lighthouse accounts, each pushing the limits of what’s possible with GenAI.  
• Engage directly with companies from AI Natives at the cutting edge to large enterprises transforming their industries, to deeply understand their needs, goals, and requirements. 
• Co-architect solutions— partner with Solutions Architects, Account Managers, and our Engineering and Product teams to design tailored solutions that leverage our 10x faster speed advatanges to transform customer applications.  
• Directly advise on customers’ long-term AI strategies 
• Become a go-to-market ninja. You will be co-owning the end-to-end customer journey, working across Sales, Solutions Architects, Marketing, Engineering, and Product teams to convert interest into long-term usage and expansion. As part of this, you will also be continuously helping to improve and optimize our processes.  
• Identify new collaboration opportunities and use cases within accounts to expand Cerebras’ partnership with them. 
• Drive the product roadmap, working closely with engineering, ML, and other product teams across the company to bring your deep understanding of customer requirements to drive future feature development.  

Skills & Qualifications  

• Strong technical background (CS/EE background, or prior experience as a SWE), and familiarity with LLMs, inference needs, agents, etc.   
• 5+ years of experience as a product manager or SWE, currently at or above the level of Senior PM or SWE. Ideally, on a developer-facing product.   
• Excellent ability to communicate with customers and navigate complex, high-stakes scenarios. 
• Ability to thrive in a fast-paced, dynamic environment.  
• Self-starter with an entrepreneurial sense of ownership of overall team and product success, and the ability to make things happen around you. A bias towards getting things done, owning the solution, and driving problems to resolution.  
• Deep passion for creative problem solving and customer success.  

Preferred requirements  

• Experience with LLM serving stacks (vLLM, TensorRT-LLM, TGI), agent frameworks, etc.  
• Interest in developer platforms and tooling. 
• MBA of equivalent professional experience. 

You’ll thrive in this role if you: 

• Are T-shaped 
• Take pride in habitual excellence 
• Love working with customers to drive impact and outcomes end-to-end 
• Are passionate about innovative technology and the potential of AI to transform how we live and work for the better 
• Are a former engineer who wants to have direct influence and impact on the business 

Location   

• Hybrid at our Sunnyvale, CA office. 
• Remote possible for candidates willing to travel 1-2x per quarter.   

About The Role 

As an ML QA Lead, you ensure quality of Cerebras SW across all supported ML workloads and workflows. You will be part of MIQ (ML Integration and Quality) team that will focus on SW components feature testing, ML training accuracy and performance, pre deployment/production validation, validating customer workloads and workflows.

As part of this role, you will influence the best testing practice, good debugging methodology, effective cross team communication and advocate for world-class products.

Responsibilities

• Drive quality of various software and hardware components of Cerebras solution to ensure accuracy, performance and usability of model trainings.
• Bring good testing methodology, effective communication and strong debugging skills to the team.
• Demand the highest quality from all components within the Cerebras environment.
• Ability to automate workflows, setup testbeds and build tools to effectively monitor and debug issues.
• Implement creative ways to break Cerebras software and identify potential problems.
• Break down complex tasks into smaller tasks. Be a problem solver. Be a thought leader.
• Ability to work in a fast-paced environment and make the necessary prioritizations and judgements which affects productivity at a company level.

Skills & Qualifications

• 8 years of relevant industry experience in Software quality and testing areas.
• Experience testing AI/ML models and evaluation of the model quality.
• Stong automation and programming skills using one or more programming languages like Python, C++ or go.
• Experience in testing compute/machine learning/networking/storage systems within a large-scale enterprise environment.
• Experience in debugging issues across scale out deployment.
• Experience in putting together thorough test-plans.
• Experience working effectively across teams, including product development, product management, customer operations, and field teams.

Preferred Skills & Qualifications

• Knowledge of ML workflows and frameworks.
• Knowledge of basic storage and networking protocols.
• Hands-on experience with training LLMs.
• Hands-on experience working with containers, Kubernetes.

About The Role

The Senior Cost Accounting Manager will thrive in a fast-paced, dynamic environment supporting both Finance and Operations. This role requires excellent communication skills, strong computational and analytical ability, and the capacity to quickly derive insights from high volume manufacturing and inventory-related transactions. The ideal candidate is proactive, detail-oriented, and comfortable owning end-to-end cost accounting processes. This is a high visibility role working closely with leaders in Supply Chain, Procurement and Finance.

Responsibilities

Cost Capitalization & Allocations

• Assess, capitalize, and allocate inventory rebates.
• Evaluate and allocate wafer manufacturing yield losses.
• Capitalize operational costs and manufacturing overhead, ensuring accurate classification for variance analysis.
• Book monthly facilities allocations.
• Accrue and capitalize freight and tariff costs monthly.

Inventory Accounting & Reconciliations

• Prepare monthly inventory reconciliations and post all related journal entries.
• Record and reconcile inventory-in-transit accruals.
• Ensure proper inventory classification across Raw Materials (RM), Work-in-Process (WIP), and Finished Goods (FG).
• Coordinate physical inventory counts with Operations and external auditors.
• Optimize and drive efficiency and accuracy in the accounting close process for inventory and other manufactured assets

Manufacturing & COGS Analysis

• Track and analyze FG movements to ensure correct accounting for COGS, RMAs, RMA replacements, and internal deployments to PPE.
• Review COGS transactions for completeness and accuracy.
• Perform gross margin flux analysis and partner with Operations to investigate key drivers.

Audit & Cross-Functional Support

• Support internal and external audit requests related to inventory and cost accounting.
• Collaborate with Operations to validate inventory movements, yields, and system accuracy.
• Support inventory count validations working cross functionally with operations.

Skills And Qualifications   

• Bachelor’s degree in Accounting, Finance, or related field
• CPA or CMA required with knowledge of GAAP, inventory accounting, costing methodologies, and fixed assets.
• 7+ years of progressive cost accounting experience in manufacturing, hardware, or infrastructure-intensive environments. Up to 3 years of Big 4 early career experience will be viewed favorably.
• Strong experience with NetSuite and advanced Excel skills.

Personal Attributes

• Thrives in fast-paced, high growth, high-ambiguity environments.
• Ability to work with high volumes of unstructured data and create appropriate data structures to provide insights
• Able to think systematically and optimize processes.
• Able to ramp quickly, identify gaps, and take ownership.
• Hands-on, detail-oriented, and execution-focused.
• Strong cross-functional communicator with sound judgment.

About The Role

We are seeking a detail-oriented and experienced Senior GL Accountant to join our corporate accounting team. This role is responsible for participating and maintaining general ledger accounting operations, ensuring accurate and timely financial reporting in compliance with U.S. GAAP.

Responsibilities  

• Perform the monthly, quarterly, and annual financial statement close processes globally in accordance with US GAAP, including preparation and review of journal entries, account reconciliations, and variance analysis for cash, prepaids, accruals, inter-company, OPEX and various other accounts.
• Support the external reporting function by providing account reconciliation and analysis reports for statements of cash flow and financial statement footnotes and disclosures preparation.
• Support quarterly, annual and interim external audits including preparation of audit schedules and responding to auditor requests.
• Support Senior Accounting Manager for the day-to-day financial activities (including chart of accounts maintenance, foreign subsidiaries, and intercompany accounting) for accuracy while ensuring compliance with US GAAP, local statutory accounting requirements and internal policies.
• Maintain effective internal controls over general ledger accounting records including account reconciliations and journal entries
• Collaborate with other departments to ensure that accounting information is accurate and timely.
• Implement best practices and ensure compliance with US GAAP, and policies and procedures.
• Ad hoc projects as needed.

Skills & Qualifications  

• Bachelor’s degree in accounting, Finance, or related field (required).
• CPA certification (preferred).
• 5+ years of progressive accounting experience, including 2+ years in a public company environment is preferred.
• Ability to solve problems and work with large volumes of data.
• Must have an understanding of internal controls and auditing processes.
• Proficiency in ERP systems skills and strong knowledge of NetSuite is a must.
• Advanced Microsoft Excel skills; ability to work with large data sets and pivot tables.
• Strong communication and collaboration skills to work cross-functionally.

The Role

As a Senior Hardware Technical Program Manager at Cerebras, you will spearhead operational excellence for our high-performance AI compute systems and data centers. You will own the end-to-end hardware schedule for design and engineering improvements, report on engineering issues, and define mitigation strategies. You will own the schedule, implementation, and software integration of hardware changes. You will collaborate closely with electrical and system engineering, manufacturing, supply chain, and system software to drive end-to-end schedule of improvements to our wafer-scale engine supercomputers. Your role will be critical in ensuring seamless translation of product strategy and engineering constraints into the creation and execution of massive supercomputer deployments in the US and abroad.

Responsibilities 

• Own end-to-end program schedule for engineering improvements. Develop and manage comprehensive program plans, including schedules, material plans, and validation plans.
• Work with key stakeholders in product management, quality, reliability, supply chain, and executive teams to define program strategy and requirements.
• Identify and mitigate technical risks, ensuring program deliverables are met on time and within budget.
• Act as the single-threaded owner across product, engineering, supply chain, and software to manage both detailed and high-level product planning.
• Develop and implement new processes that improve team efficiency as the business scales.
• Collaborate with supply chain, operations, and procurement teams to coordinate component sourcing, vendor management, and logistics.
• Identify and implement strategies to manage costs effectively while meeting performance and efficiency targets.
• Present program status, metrics, and operational risks to senior leadership

Requirements

• Bachelor’s or Master’s degree in computer science, electrical engineering, mechanical engineering, physics, mathematics, a related scientific/engineering discipline, or equivalent practical experience. 
• 8+ years of experience in technical program management, with a focus on hardware, embedded SW, and product planning.
• Strong understanding of hardware design processes, product lifecycle management, and manufacturing workflows. 
• Strong understanding of the intersection of software and hardware, and how hardware changes affect software workflows.
• Proven experience effectively managing project priorities across a diverse set of hardware, software, product, and operations disciplines.
• Demonstrated ability to work effectively with highly technical cross-functional engineering teams.
• Familiarity with project management software, such as MS Project or Smartsheet.
• Strong business acumen with experience in cost management and analysis.
• Outstanding verbal and written communication skills.

The base salary range for this position is $180,000 to $230,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

Senior IC Design Engineer – IO Signal Integrity & Power Delivery 

About the Role 

In this role, you’ll be at the center of high-speed IO interface design and integration, driving the signal integrity (SI) and power delivery (PI) performance of custom IP within our wafer-scale engine. 

This position emphasizes complete system analysis, architecture, integration and circuit design from transistor level to external voltage regulator, to ensure that custom and third-party IP meets performance, power, and reliability targets across die, 3d assembly, and system level boundaries. 

You’ll collaborate closely with design, packaging, and system engineers to architect and validate custom DDR-like interfaces, IO circuits, and power delivery networks. This is a hands-on technical leadership role for an engineer who understands how circuit behavior, interconnect design, and system integration combine to define product success. 

Key Responsibilities 

• Own IO signal integrity and power delivery analysis for custom and third-party IP integration in full system stack: die level, 3d integration, board level 

• Define interface architecture and design specifications, including signaling schemes, impedance targets, and power distribution requirements. 

• Perform and review channel modeling, IBIS-AMI/SPICE simulations, and system-level SI/PI analysis to ensure timing and margin robustness. 

• Collaborate with internal and external IP providers to evaluate, select, and integrate custom IO and PHY solutions. 

• Lead power delivery network (PDN) modeling and IR-drop analysis, driving improvements across chip, package, and board. 

• Support silicon bring-up, validation, and correlation of simulation results to lab measurements. 

• Provide technical direction on ESD design, IO reliability, and aging (NBTI, PBTI, HCI). 

• Partner with architecture, physical design, and system teams to optimize signal quality, timing closure, and power efficiency. 

• Develop and maintain internal simulation flows, modeling scripts, and automation tools in Tcl/Python. 

Skills & Qualifications 

• 10+ years of experience in IC or IO design, analysis, or integration. 

• Deep understanding of signal integrity, power integrity, and high-speed interface design (DDR, LPDDR, HBM, or similar). 

• Experience with 3d or 2.5d integration, interposers, die stacking 

• Strong knowledge of FinFET CMOS technology and transistor-level device behavior. 

• Expert with HSPICE, FineSim, or equivalent circuit and transient simulation tools. 

• Experience with channel and package modeling, S-parameter extraction, and time/frequency-domain analysis. 

• Proficient in IR-drop analysis, PDN optimization, and decoupling network design. 

• Solid understanding of IO and ESD circuit fundamentals, including protection and clamp strategies. 

• Experience running aging and reliability simulations and applying results to design optimization. 

• Strong scripting and automation experience in Tcl, Python, or similar. 

• Excellent problem-solving, analytical, and cross-functional collaboration skills. 

• B.S. or M.S. in Electrical Engineering or equivalent required (Ph.D. preferred). 

The base salary range for this position is $200,000 to $275,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role

As a Senior Mechanical Engineer at Cerebras, you will lead the design of mechanical systems for our next-generation wafer-scale engine. Your responsibilities will include ensuring compliance with specifications, validating manufacturability, and delivering a high-quality product in a fast-paced environment—tackling some of the most challenging problems in the rapidly evolving AI space.

In this role, you will develop mechanical infrastructure for Cerebras’ custom hardware system.

• Rapidly iterate on designs and analysis to inform high level systems trades and steer overall product direction.
• Provide comprehensive support for environmental and performance testing on hardware, validating analyses, and ensuring compliance with design criteria.
• Ownership of technical deliverables within.
• Conduct first article inspections, functional analysis, identify and resolve issues.
• Collaborate across design-manufacturing-production, diagnostic and embedded software engineering teams, contractors and suppliers.
• Perform detailed structural analysis to engineer robust.

Responsibilities

• Work in a fast paced, high energy environment with short product development cycles on many challenging electro-mechanical projects.
• Drive and own all design aspects of electro-mechanical systems from concept to bring-up and production.
• Work with cross functional teams to gather all design requirements to lead the mechanical design, troubleshoot, and create & release all deliverables including drawings, BOMs, etc.
• Work closely with thermal engineers to solve mechanical challenges.
• Evaluate materials and components to meet performance, schedule and cost targets.
• Work closely with vendors to resolve any DFM/A issues quickly.

Requirements

• • 10+ years of experience as a Mechanical Design Engineer.
  • BS in Mechanical Engineering.
  • Strong analytical, diagnostic and problem-solving skills.
  • Very strong in 3D solids modeling CAD package, proficiency with Solidworks would be preferable.
  • Proficient with PLM system, such as Arena or Agile.
  • Proficient in CTF dimensioning and GD&T.
  • Must be able to do full Tolerance Analysis of complex systems and have working knowledge of FEA.
  • Great team player with strong interpersonal & communication skills.
  • Experience in electronic liquid cooling is a plus.
  • Must have in-depth knowledge of all latest fabrication processes; sheet metal, machining, die-casting, injection molding, and 3D printing.

About the Role

We are looking for a Software Engineer to join the ML Integration and Quality team at Cerebras. This team sits at the intersection of machine learning infrastructure, distributed systems, and hardware/software co-design.

In this role, you will help integrate and validate the software stack that powers the Cerebras AI platform, ensuring large-scale ML workloads run reliably and efficiently across our systems. You will work closely with engineers across runtime, compiler, kernel, and hardware teams to debug complex issues, improve automation, and strengthen the reliability of our AI infrastructure.

This is an excellent opportunity for engineers who enjoy working across the stack, debugging complex systems, and improving the reliability of large-scale AI platforms.

Responsibilities

• Integrate and validate software components across the Cerebras AI platform.
• Collaborate with engineers across ML runtime, compiler, kernel, and hardware teams to ensure reliable feature integration.
• Investigate and debug complex issues across distributed systems and large-scale ML workloads.
• Build automation tools and infrastructure to support integration testing, system validation, and debugging workflows.
• Develop and maintain testbeds used to validate system performance and reliability.
• Identify system bottlenecks, failure points, and edge cases that impact ML workload performance.
• Contribute to test plans and validation strategies for new features and platform capabilities.
• Improve observability, diagnostics, and debugging workflows across the ML software stack.
• Work with product and engineering teams to ensure high-quality releases of the Cerebras inference platform.

Minimum Skills & Qualifications

• ~5 years of experience in software engineering, systems engineering, or infrastructure development.
• Strong programming skills in Python, C++, Go, or similar languages.
• Experience debugging complex systems or distributed software environments.
• Familiarity with systems-level development, infrastructure tooling, or platform integration.
• Experience building automation tools, testing frameworks, or internal developer tooling.
• Strong problem-solving skills and the ability to investigate issues across multiple system layers.
• Excellent communication and collaboration skills.

Preferred Skills

• Experience working with machine learning infrastructure or ML model deployment.
• Familiarity with LLM or multimodal model workloads.
• Experience with distributed systems, cloud infrastructure, or large-scale compute clusters.
• Exposure to performance debugging, profiling, or system observability tools.
• Experience with microservices, containerized environments, or cluster orchestration.
• Exposure to hardware accelerators, compilers, or ML frameworks.

Location

• This role follows a hybrid schedule, requiring in-office presence 3 days per Please note, fully remote is not an option.
• Office locations: Sunnyvale, CA or Toronto, ON. 

About the Role
We are seeking a versatile and experienced engineer to join our SOTA Training Platform team. This team is responsible to rapidly bring up state-of-the-art open-source models (like LLaMA, Qwen, etc) or customer-provided proprietary models on our Cerebras CSX systems. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire Cerebras software stack.
Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications.

• Contribute to the end-to-end bring up of ML models on Cerebras CSX systems.
• Work across the stack: model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning.
• Debug performance and correctness issues spanning model code, compiler IRs, runtime behavior, and hardware utilization.
• Propose and prototype improvements across tools, APIs, or automation flows to accelerate future bring ups.
• Study emerging training and post-training algorithms and map to Cerebras software architecture and hardware.

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
• 5+ years of relevant industry experience (internship/co-op experience included)
• Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
• Strong debugging skills across performance, numerical accuracy, and runtime integration.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion).
• Proficiency in C/C++ programming and experience with low-level optimization.
• Proven experience in compiler development, particularly with LLVM and/or MLIR.
• Strong background in optimization techniques, particularly those involving NP-hard problems.
• Familiarity with large scale ML systems and state of the art algorithms, including model training and reinforcement learning.

• Competitive salary and benefits package.
• Opportunities for professional growth and career advancement.
• A dynamic and innovative work environment.
• The chance to work on cutting-edge technologies and make a significant impact on the future of AI.

About The Role

We are building the next generation of large-scale AI systems that power training and inference workloads at unprecedented scale and efficiency.

You will design and develop high-performance distributed software that orchestrates massive compute and data pipelines across heterogeneous clusters. Your work will push the limits of concurrency, throughput, and scalability—enabling efficient execution of models at massive scale. This role sits at the intersection of systems engineering and machine learning performance, demanding both architectural depth and low-level implementation skills. You will help shape how models are executed and optimized end-to-end, from data ingestion to distributed execution, across cutting-edge hardware platforms.

We’re hiring for runtime roles across both Training and Inference.

Responsibilities

• Design and implement distributed runtime components to efficiently manage large-scale execution workloads.
• Develop and optimize high-performance data and communication pipelines that fully utilize CPU, memory, storage, and network resources.
• Enable scalable execution across multiple compute nodes, ensuring high concurrency and minimal bottlenecks.
• Collaborate closely with ML and compiler teams to integrate new model architectures, training regimes, and hardware-specific optimizations.
• Diagnose and resolve complex performance issues across the software stack using profiling and instrumentation tools.
• Contribute to overall system design, architecture reviews, and roadmap planning for large-scale AI workloads.

Skills & Qualifications

• 3+ years of experience developing high-performance or distributed system software.
• Strong programming skills in C/C++, with expertise in multi-threading, memory management, and performance optimization.
• Experience with distributed systems, networking, or inter-process communication.
• Solid understanding of data structures, concurrency, and system-level resource management (CPU, I/O, and memory).
• Proven ability to debug, profile, and optimize code across scales—from threads to clusters.
• Bachelor’s, Master’s, or equivalent experience in Computer Science, Electrical Engineering, or related field.

Preferred Skills & Qualifications

• Familiarity with machine learning training or inference pipelines, especially distributed training and large-model scaling.
• Exposure to Python and PyTorch, particularly in the context of model training or performance tuning.
• Experience with compiler internals, custom hardware interfaces, or low-level protocol design.
• Prior work on high-performance clusters, HPC systems, or custom hardware/software co-design.
• Deep curiosity about how to unlock new levels of performance for large-scale AI workloads.

The Role:
In this exciting role, you will be responsible for bring up and optimizations of Cerebras’s Wafer Scale Engine (WSE). Suitable candidate will have experience delivering end to end solutions working closely with teams across chip design, system performance, software development and productization.

Responsibilities:

• On Wafer Scale Engines, develop and debug flows that embed well tested and deployable optimizations in production processes to reduce time and costs 
• Work on refining AI Systems across H/W-S/W design constraints such as di/dt, V-F characterization space, current and temperature limits in relation to optimizations for performance.
• Develop/Enhance infrastructure to enable silicon for real world workload testing
• Develop self-checking metrics, as well as instrumentation for debug and coverage
• Work with the silicon architects/designers, performance engineers and software engineers to enhance performance of Wafer Scale Engines.
• Work across domains such as, Software, Design, Verification, Emulation & Validation to refine and optimize performance and process.
• Work with CI/CD tools, git repositories, github, git actions/Jenkins, merge and release flows to streamline test and release.

Skills & Qualifications:

• BS/BE/B.Tech or MS/M.Tech in EE, ECE, CS or equivalent work experience
• 7-10+ years of industry experience
• 3-5 years of experience in Pre-silicon & Post Silicon ASIC hardware
• Good understanding of computer architecture and networking
• Excellent Coding in languages such as Python/Verilog/System Verilog and C
• Proficient in hardware/software codesign and layered architectures.
• Excellent debugging, analytical, and problem-solving skills
• Proficient in large scale testing and automation using pytest and python
• Good presentation skills to refine diverse information and put forth optimization strategies and results.
• Good interpersonal skills, ability & desire to work as a standout colleague
• Proven track record of working cross-functionally learning fast and driving issues to closure 

Preferred:

• Previous work in AI-ML with 100+ CPU core & communication fabric-based design.
• Familiarity with in-line testing and diagnostics using CPU memory and execution with self-checking.
• Knowledge of chip defect profiles and mitigation strategies across the hardware and software stack
• Familiarity in creating test and s/w infrastructure at large scale
• Working across global time zones 

Location:

Sunnyvale, California.

Bangalore, India

Toronto, Canada 

The base salary range for this position is $175,000 to $275,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

The Role:
In this exciting role, you will be responsible for bring up and optimizations of Cerebras’s Wafer Scale Engine (WSE). Suitable candidate will have experience delivering end to end solutions working closely with teams across chip design, system performance, software development and productization.

Responsibilities:

• On Wafer Scale Engines, develop and debug flows that embed well tested and deployable optimizations in production processes to reduce time and costs 
• Work on refining AI Systems across H/W-S/W design constraints such as di/dt, V-F characterization space, current and temperature limits in relation to optimizations for performance.
• Develop/Enhance infrastructure to enable silicon for real world workload testing
• Develop self-checking metrics, as well as instrumentation for debug and coverage
• Work with the silicon architects/designers, performance engineers and software engineers to enhance performance of Wafer Scale Engines.
• Work across domains such as, Software, Design, Verification, Emulation & Validation to refine and optimize performance and process.
• Work with CI/CD tools, git repositories, github, git actions/Jenkins, merge and release flows to streamline test and release.

Skills & Qualifications:

• BS/BE/B.Tech or MS/M.Tech in EE, ECE, CS or equivalent work experience
• 7-10+ years of industry experience
• 3-5 years of experience in Pre-silicon & Post Silicon ASIC hardware
• Good understanding of computer architecture and networking
• Excellent Coding in languages such as Python/Verilog/System Verilog and C
• Proficient in hardware/software codesign and layered architectures.
• Excellent debugging, analytical, and problem-solving skills
• Proficient in large scale testing and automation using pytest and python
• Good presentation skills to refine diverse information and put forth optimization strategies and results.
• Good interpersonal skills, ability & desire to work as a standout colleague
• Proven track record of working cross-functionally learning fast and driving issues to closure 

Preferred:

• Previous work in AI-ML with 100+ CPU core & communication fabric-based design.
• Familiarity with in-line testing and diagnostics using CPU memory and execution with self-checking.
• Knowledge of chip defect profiles and mitigation strategies across the hardware and software stack
• Familiarity in creating test and s/w infrastructure at large scale
• Working across global time zones 

Location:
Bangalore, India

Toronto, Canada

Sunnyvale, California.

For Sunnyvale: The base salary range for this position is $175,000 to $275,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

About The Role

This Sr. TPM role owns site and data center operations programs supporting Cerebras’ AI Cloud and customer deployments. The position sits at Sunnyvale HQ and works closely with Hardware Engineering, Inference Engineering, and Operations leadership to ensure Cerebras systems are reliably deployed, operated, and scaled.

This is a highly technical, execution-focused TPM role with strong emphasis on operational readiness, cross-functional coordination, and metrics/KPIs.

Responsibilities 

• Own end-to-end technical programs for data center and site operations
• Act as single-threaded owner across:
  • Hardware & Systems Engineering
  • AI Cloud Infrastructure & Operations
  • Network & Storage Engineering
  • Facilities, power, cooling, and colo partners
• Drive site readiness for Cerebras Wafer-Scale Engine systems
• Partner on installation, commissioning, change management, and break/fix workflows
• Lead incident reviews and postmortems; ensure corrective actions are closed
• Define and own operational metrics and KPIs, including:
  • Availability and reliability
  • Incident rate, severity, MTTR / MTTD
  • Deployment readiness and time-to-service
  • Capacity and operational risk
• Build executive-level dashboards and reporting
• Establish program governance, risk tracking, and RACI clarity
• Present program status, metrics, and operational risks to senior leadership

Required Background 

• 8+ years in Technical Program Management, Infrastructure Ops, or Data Center Ops
• Experience leading large, cross-functional infrastructure programs
• Strong understanding of:
  • Data center power and cooling fundamentals
  • Network and storage basics
  • Hardware-centric platforms
• Proven ability to define and operationalize metrics
• Strong written and executive-level communication skills

Preferred Experience 

• AI/ML, HPC, or accelerator-based infrastructure
• High-density and/or liquid-cooled data centers
• Working with colocation providers and facilities teams
• Incident management, reliability, or service operations background

About The Role

We are seeking a highly skilled WAN Network Engineer to design, implement, manage, and optimize global connectivity. The ideal candidate will have strong experience with carrier networks, routing protocols, and network security, and will play a critical role in ensuring high availability, performance, and reliability of global network services.

Responsibilities

• Design, deploy, and maintain WAN network across leased lines and dark fiber for low latency and 99.999% availability. 
• Collaborate with telecom providers and ISPs for circuit provisioning, upgrades, and issue resolution.
• Configure, troubleshoot, and optimize security and routing protocols (IPSec Tunnels, MACsec, BGP, VXLAN, EVPN). 
• Monitor WAN performance, latency, packet loss, capacity utilization.  Analyze traffic patterns to predict growth and trigger circuit upgrades or hardware refreshes before bottlenecks occur.
• Implement redundancy, failover, QoS, and traffic engineering to ensure business continuity.
• Participate in network modernization and cloud connectivity projects (AWS, Azure, GCP).
• Provide Tier 3 support for WAN-related incidents and root cause analysis.
• Develop and maintain network documentation, diagrams, and standard operating procedures.
• Use Python, Ansible, or Terraform to deploy configurations and manage network state at scale.
• Support network security initiatives including site-to-site VPNs and perimeter connectivity. Ensure compliance with security, governance, and operational best practices.

Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering, or Computer Engineering. Master’s degree is preferred. 
• 6+ years of experience in WAN network engineering, or Service Provider network, or Hyper-scale Data Center environment.
• Industry certifications such as, CCIE or JNCIE.  Strong knowledge of BGP routing protocols and WAN technologies.
• Experience with major network vendors such as Arista, Cisco, Juniper, or Palo Alto.
• Strong troubleshooting and analytical skills, and excellent communication and documentation skills.
• Experience with cloud networking and hybrid network architectures.
• Expertise with network automation tools (Python, Ansible, Terraform).
• Knowledge of network monitoring tools (SolarWinds, Kentik, ThousandEyes, or similar).
• Ability to participate in on-call rotation and after-hours maintenance.