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

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. 

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.

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 

About The Team 

Cerebras builds wafer-scale AI processors—single chips delivering tens of PB/s of memory bandwidth and a dataflow architecture that accelerates at a granularity no multi-device system can match. The Advanced Technology Group (ATG) is Cerebras’ pathfinding organization. We work ahead of product to explore new architectures, demonstrate breakthrough performance on scientific and AI workloads, and shape the technical roadmap for future Cerebras hardware and software. Our work regularly appears at top-tier venues (Supercomputing, SIAM, IEEE, and NeurIPS) and directly influences the design of next-generation wafer-scale systems. 

About The Role 

Most AI research today is shaped by the constraints of existing hardware. This role starts from the other direction: what would you build if the architecture let you rethink the fundamentals? You will design and develop AI models and training methodologies on wafer-scale hardware, working at the level of optimization theory, model architecture, and statistical foundations rather than assembling existing components. 

The ATG sits at the intersection of AI, computational science, and computer architecture, and your work will draw on all three. You will collaborate closely with Cerebras’ ASIC, compiler, kernel, and AI teams as well as external partners at universities and national laboratories. 

What You Will Do 

• Design AI models and training methods from first principles, leveraging architectural properties of wafer-scale hardware that are unavailable on conventional platforms. 
• Investigate how techniques from computational science—numerical methods, PDE solvers, simulation—can inform and advance AI model design, and explore hybrid workflows that couple simulation and learning. 
• Develop a deep understanding of the hardware substrate and use it to guide algorithmic choices: model structure, optimization strategy, memory access patterns, numerical precision. 
• Publish findings and present at top-tier venues (NeurIPS, ICML, ICLR, etc.); represent Cerebras in the broader AI/ML research community. 
• Inform the design of future Cerebras hardware and software by identifying the computational patterns that matter most for next-generation AI workloads. 

What We Are Looking For 

• PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field preferred; exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply. 
• Mathematical maturity: comfort with the theory behind gradient methods, loss landscapes, generalization, and the relationship between model structure and data statistics. 
• Track record of published research at top-tier AI or computational science venues. 
• Proficiency in Python and PyTorch; comfort with C or other low-level languages is a strong signal. 
• Excellent communication and interpersonal skills: able to present complex technical material to both ML and systems audiences, and to collaborate effectively in a fast-paced, small-team environment. 

Why This Opportunity Is Exciting And Unique 

• You will have direct access to hardware that changes what’s algorithmically possible. Tens of PB/s of memory bandwidth and fine-grained dataflow execution open design spaces that don’t exist on GPU clusters. 
• You will work alongside researchers in computational science, computer architecture, and performance engineering. The synthesis across these fields is central to ATG’s approach. 
• Your research will influence silicon - ATG’s findings directly shape the design of future Cerebras chips and systems. 

We are hiring for multiple positions across experience levels. If this work resonates, we encourage you to apply. 

• 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.

About The Team 

Cerebras builds wafer-scale AI processors—single chips delivering tens of PB/s of memory bandwidth and a dataflow architecture that accelerates at a granularity no multi-device system can match. The Advanced Technology Group (ATG) is Cerebras’ pathfinding organization. We work ahead of product to explore new architectures, demonstrate breakthrough performance on scientific and AI workloads, and shape the technical roadmap for future Cerebras hardware and software. Our work regularly appears at top-tier venues (Supercomputing, SIAM, IEEE, and NeurIPS) and directly influences the design of next-generation wafer-scale systems. 

About The Role 

We are seeking Compiler Engineers to join a small team of specialists working on our emerging Tungsten language compiler. Tungsten is Cerebras’ dataflow programming language, purpose-built for wafer-scale hardware. You will work on the Tungsten compiler from language design through code generation, building the toolchain that translates high-level intent into efficient execution across hundreds of thousands of cores with a memory and interconnect model unlike anything in conventional computing. 

This is not incremental work on an existing backend. The architecture is new, the programming model is new, and the compiler is where those two things meet. You will collaborate closely with Cerebras’ ASIC, kernel, and AI teams, and your design decisions will directly shape both the language and the hardware it targets. Beyond the compiler itself, the broader toolchain—runtime, debugger, simulator—is still being built, and we are equally interested in engineers who want to own those pieces of the developer experience on novel hardware. 

What You Will Do 

• Design and implement compiler passes across the Tungsten toolchain: mid-end optimization, backend code generation, instruction scheduling, register allocation, assembler, and linker. 
• Co-design language constructs that improve expressiveness and performance for dataflow execution on wafer-scale hardware. 
• Develop and iterate on code generation strategies for complex scientific and AI workloads, analyzing performance bottlenecks and closing the gap between peak and achieved throughput. 
• Extend the compiler to support future hardware architectures as they move from design to silicon. 
• Work directly with ASIC architects and application researchers to inform hardware-software co-design decisions. 

What We Are Looking For

• PhD in Computer Science or Computer Engineering preferred; exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply. 
• Substantial experience in compiler development: IR design, optimization passes, code generation, or backend implementation for novel or non-standard architectures. 
• Strong grasp of computer architecture: instruction sets, memory models, dataflow execution, and how hardware constraints shape compilation strategy. 
• Systems-level programming ability in C; comfort reasoning about performance at the instruction and memory-access level. 
• Ability to think about compilation as a design problem, not just an implementation task: you should have opinions about how language semantics, compiler IR, and hardware capabilities interact. 
• Excellent communication and interpersonal skills: able to work effectively in a small, fast-moving team where compiler, architecture, and application concerns are deeply intertwined. 

Valuable Assets 

• Experience with compilers for spatial, dataflow, or CGRA architectures where the compilation model diverges significantly from conventional CPU/GPU targets. 
• Exposure to ML compiler frameworks (MLIR, XLA, TVM) and understanding of how AI workloads map to hardware. 
• Experience with multi-dimensional data representations, tiling strategies, and vectorized operations. 
• Track record of published research or patents in compilers, programming languages, or architecture. 
• Experience building runtime systems, debuggers, or architecture simulators, particularly for non-standard hardware. 
• Understanding of parallel/distributed systems and cluster computing. 

Why This Opportunity Is Exciting And Unique 

• Build a compiler for hardware that doesn’t exist anywhere else. The architecture is the constraint and the opportunity. 
• Publish and open-source your research. We present at Supercomputing, SIAM, IEEE, NeurIPS, and beyond. 
• Work on the fastest AI system in the world, with direct access to the hardware your compiler targets. 
• Join at a pivotal moment: Cerebras is pre-IPO with strong commercial traction and rapid growth. 
• Be part of a small, technical team with high autonomy, minimal bureaucracy, and a culture that values depth over hierarchy. 

We are hiring for multiple positions across experience levels. If this work resonates, we encourage you to apply. 

About The Team 

Cerebras builds wafer-scale AI processors—single chips delivering tens of PB/s of memory bandwidth and a dataflow architecture that accelerates at a granularity no multi-device system can match. The Advanced Technology Group (ATG) is Cerebras’ pathfinding organization. We work ahead of product to explore new architectures, demonstrate breakthrough performance on scientific and AI workloads, and shape the technical roadmap for future Cerebras hardware and software. Our work regularly appears at top-tier venues (Supercomputing, SIAM, IEEE, and NeurIPS) and directly influences the design of next-generation wafer-scale systems. 

About The Role 

We are seeking R&D Engineers to join Cerebras' Advanced Technology Group. You will design and implement workloads that establish new performance benchmarks on wafer-scale hardware, leveraging architectural features that no traditional platform offers. The 
scope ranges from large-scale scientific simulations to emerging AI/ML models, and the work sits at the intersection of algorithm design, compiler co-optimization, and hardware architecture. You will collaborate closely with Cerebras’ ASIC, compiler, kernel, and AI teams as well as external partners at universities and national laboratories. 

What You Will Do 

• Design and implement challenging scientific computing and AI workloads on Cerebras’ Wafer-Scale Engine, targeting performance results that advance the state of the art. 
• Lead algorithm–hardware co-design efforts with internal R&D teams and external research partners, turning architectural capabilities into measurable application-level advantages. 
• Build analytical performance models that quantify bottlenecks, guide optimization, and inform future chip and compiler design decisions. 
• Contribute to Cerebras’ multi-year technology roadmap by identifying high-impact workloads, proposing architectural experiments, and validating them on silicon. 
• Publish findings and present at top-tier conferences and journals; represent Cerebras in the broader HPC and AI research communities. 

What We Are Looking For 

• PhD in Computer Science, Engineering, Applied Mathematics, Physics, or a related quantitative field preferred. Exceptional candidates without a graduate degree who demonstrate equivalent depth through published research, significant open-source contributions, or a strong industry track record are encouraged to apply. 
• Deep experience in at least one of the following: computer architecture and accelerator design; parallel, distributed, or high-performance computing; numerical methods and scientific simulation; AI/ML theory and model design at a mathematical level. 
• Strong ability to analytically model and optimize the performance of complex systems and algorithms. 
• Track record of published research or patents in relevant venues. 
• Proficiency in C and Python; comfort working close to hardware. 
• Excellent communication and interpersonal skills: able to present complex technical material to both specialist and cross-functional audiences, and to collaborate effectively in a fast-paced, small-team environment. 

Areas Of Particular Interest 

We are hiring across several focus areas. Exceptional depth in one or more of the following is a strong signal: 

• Computational science: researchers who can bring insights from numerical methods and simulation into AI, or couple simulation and learning into joint computational workflows. Depth in hydrodynamics, solid mechanics, electromagnetics, molecular dynamics, or related PDE-based fields. 
• AI/ML foundations: deep understanding of model architecture, optimization methods, and their statistical underpinnings—the ability to design from first principles, not just apply established recipes. 
• Computer architecture: microarchitecture design, computing paradigms at the circuit and datapath level, memory hierarchy design. 
• Performance engineering: roofline modeling, bandwidth analysis, kernel optimization, communication-computation overlap, and compiler-level tuning for novel hardware. 

Why This Opportunity Is Exciting And Unique 

• Build on a fundamentally different architecture, unconstrained by GPU assumptions. 
• Publish and open-source your research. We present at Supercomputing, SIAM, IEEE, NeurIPS, and beyond. 
• Work on the fastest AI system in the world, with direct access to the hardware your compiler targets. 
• Join at a pivotal moment: Cerebras is pre-IPO with strong commercial traction and rapid growth. 
• Be part of a small, technical team with high autonomy, minimal bureaucracy, and a culture that values depth over hierarchy. 

We are hiring for multiple positions across experience levels. If this work resonates, we encourage you to apply. 

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.

About The Role

We are seeking a highly skilled and experienced AI Infrastructure Operations Engineer to manage and operate our cutting-edge machine learning compute clusters. These clusters would provide the candidate an opportunity to work with the world's largest computer chip, the Wafer-Scale Engine (WSE), and the systems that harness its unparalleled power. 

You will play a critical role in ensuring the health, performance, and availability of our infrastructure, maximizing compute capacity, and supporting our growing AI initiatives. This role requires a deep understanding of Linux-based systems, containerization technologies, and experience with monitoring and troubleshooting complex distributed systems. The ideal candidate is a proactive problem-solver with expertise in large-scale compute infrastructure, dependable and an advocate for customer success.  

Responsibilities

• Manage and operate multiple advanced AI compute infrastructure clusters. 
• Monitor and oversee cluster health, proactively identifying and resolving potential issues. 
• Maximize compute capacity through optimization and efficient resource allocation. 
• Deploy, configure, and debug container-based services using Docker. 
• Provide 24/7 monitoring and support, leveraging automated tools and performing hands-on troubleshooting as needed. 
• Handle engineering escalations and collaborate with other teams to resolve complex technical challenges. 
• Contribute to the development and improvement of our monitoring and support processes. 
• Stay up-to-date with the latest advancements in AI compute infrastructure and related technologies. 

Skills And Requirements

• 6-8 years of relevant experience in managing and operating complex compute infrastructure, preferably in the context of machine learning or high-performance computing. 
• Strong proficiency in Python scripting for automation and system administration. 
• Deep understanding of Linux-based compute systems and command-line tools. 
• Extensive knowledge of Docker containers and container orchestration platforms like k8s and SLURM. 
• Proven ability to troubleshoot and resolve complex technical issues in a timely and efficient manner. 
• Experience with monitoring and alerting systems. 
• Should have a proven track record to own and drive challenges to completion. 
• Excellent communication and collaboration skills. 
• Ability to work effectively in a fast-paced environment. 
• Willingness to participate in a 24/7 on-call rotation. 

Preferred Skills And Requirements

• Operating large scale GPU clusters.
• Knowledge of technologies like Ethernet, RoCE, TCP/IP, etc. is desired.
• Knowledge of cloud computing platforms (e.g., AWS, GCP, Azure).
• Familiarity with machine learning frameworks and tools.
• Experience with cross-functional team projects. 

Location 

• SF Bay Area.
• Toronto, Canada.
• Bangalore, India.

About The Role

Cerebras Systems is a pioneer in large-scale AI Supercomputers. These multi-exaflop supercomputers are deployed in some of the biggest datacenters. These supercomputers are built using our Wafer-Scale Cluster technology - a cluster of several Wafer Scale Engine (WSE) chips. The Cluster engineering team is responsible for delivering software that are all-things related to cluster.

Responsibilities

• Automate bare-metal configuration of networking, OS, and application software in large clusters of Cerebras WSE, servers, and switches.
• Additional push button workflows for cluster upgrades, downgrades, and security patching with key metrics to minimize downtime on clusters.
• An orchestration and scheduler system for resource allocation, job submission C placements for a multi-user environment on a cluster.
• Seamless support for both on-premise and cloud mode deployment and operations.
• A robust system for monitoring, detecting and handling failures for a variety of resources on the clusters (including High Availability of clusters).
• Broad cluster and job monitoring and visualization capabilities, along with alerting systems.
• User facing tools to monitor the status of jobs and collect metrics.
• Administrator facing tools to manage and operate large clusters.

Skills & Qualifications

• Strong track record of software architecture, system design and development.
• Strong track record of development in distributed cluster.
• Strong understanding of Kubernetes (K8s) software ecosystem, Prometheus and Grafana.
• Strong development skills in GoLang, Python, bash.
• Strong debugging skills with distributed systems.
• Strong skill to develop tests for the new features and regress old features.

About the Role 

We are building a high-performance SRE function to support one of the world’s fastest-growing AI inference services, powered by the Wafer-Scale Engine (WSE), helping deliver infrastructure for frontier-class models from leading model builders such as OpenAI. 

This role offers immediate ownership of real production systems at a growing scale, direct mentorship from seasoned engineers, and close collaboration with incoming Staff SREs who will focus on long-term automation. After ~1 month of shared hands-on operations with the Staff engineers, you’ll primarily operate the current setup, bring up new capacity in high-stakes environments and help bring new continuous delivery pipelines into production use. 

If you thrive in high-ownership SRE roles at scale and want to help shape a team from the ground up in cutting-edge AI Inference infrastructure, this is your chance. 

This role does not require 24/7 on-call rotations. 

Key Responsibilities 

• Remain hands-on with operational execution (releases, capacity changes, cluster upgrades) over the next year as we build robust continuous delivery pipelines and self-service capabilities 

• Contribute to the development of self-service CD pipelines for key workflows using our stack: Kubernetes, Bazel, Prometheus/Grafana/InfluxDB, Python, and Go. 

• Build reusable automation and internal developer tools that minimize operational toil and cross-team friction 

• Develop and extend telemetry, observability and alerting solutions to ensure operational reliability at scale 

• Collaborate with Cluster Ops and development teams to identify high-impact automation opportunities and iterate quickly 

• Contribute to reliability practices (SLOs, post-mortems, capacity planning) 

Required Experience & Skills 

• 2-4+ years in SRE with a strong operations or automation focus 

• Production Kubernetes experience 

• Solid Python or Go for building tools and automation 

• Proficiency with Prometheus, Grafana, and observability-driven workflows 

• Ability to measure and communicate impact – reliability metrics, operational toil, velocity gains 

Nice-to-Have 

• Hands-on GitOps expertise, Argo CD / Flux or equivalent, is a plus 

• Experience with building continuous delivery pipelines is a strong plus 

• Experience with Bazel or similar build systems is a strong plus. 

• Familiarity with capacity planning, on-prem or multi-datacenter environments 

Location   

• SF Bay Area 

• Toronto 

About The Role

We are hiring a Head of IT to build and run the internal technology backbone of a company that is scaling quickly and operating at the edge of AI hardware and software. This is not a steady-state IT leadership job, It is a build-and-scale role for someone who thrives when the ground is moving. 

You will own the systems that Cerebras employees, contractors, and executives rely on every day: laptops, identity, SaaS, networking, collaboration, endpoint security, internal support, and the IT controls that a company of our maturity needs to have in place. You will keep a highly technical, impatient engineering population unblocked while hardening the environment to standards expected of a company at our stage, including SOX-grade ITGCs and SOC 2. 

Responsibilities 

• Global IT strategy, roadmap, and budget across corporate IT, end-user computing, collaboration, identity, and internal networking. 
• Scaling the IT organization as headcount grows, including hiring, leveling, and building regional coverage across US, EMEA, and APAC. 
• Identity and access management: SSO, MFA, lifecycle automation, least-privilege, and joiner-mover-leaver workflows across a fast-changing org. 
• Endpoint fleet management across macOS, Windows, and Linux, with zero-touch provisioning, MDM, patching, and a hardened baseline that works for AI research workflows. 
• SaaS governance: rationalizing and integrating the SaaS portfolio, running vendor selection, contracts, renewals, and spend control. 
• Corporate networking, Wi-Fi, VPN/ZTNA, and office buildouts as sites open and expand. 
• Executive support for leadership, board, and investor-facing interactions. 
• IT service management: ticketing, SLAs, on-call, knowledge base, and employee-experience metrics. 
• IT controls and audit readiness, partnering with Security, Legal, and Finance. 
• Business continuity, disaster recovery, and incident response for corporate systems.

Skills And Qualifications   

• 10+ years in corporate IT with at least 5 years leading IT at a high-growth technology company, including experience scaling through 2-5x headcount growth. 
• Direct hand-on experience with AI coding agents, LLM’s, scaling operations with AI supporting tooling. 
• Hands-on experience operating IT in an environment subject to SOX ITGCs or equivalent control frameworks, including working directly with internal and external auditors. 
• Strong technical depth across identity (Okta, Entra), MDM (Jamf, Intune), networking, and modern endpoint security. You can go toe-to-toe with your engineers, not just manage them. 
• Track record of building IT teams that engineering respects. 
• Ability to move quickly without breaking compliance posture. You know where a manual process is fine for now and where it is not. 
• Good judgment on buy-vs-build and on when to automate vs. when to ship a process and iterate. 
• Direct, clear communicator, comfortable briefing the CEO, CFO, and Audit Committee. 

Nice-to-haves 

• Experience with hardware labs, manufacturing, hardware engineering etc. 
• Experience supporting AI/ML research organizations or semiconductor and hardware engineering teams. 
• Exposure to global expansion, including EMEA and the Middle East. 
• Experience working in environments with sensitive IP and export-control considerations. 

How You Work 

• Bias to action. In a company moving at this pace, a slow decision usually costs more than a wrong one. 
• Focused on the people you support. Your job is to take friction out of their day. 
• Pragmatic about technology. You pick boring, reliable tools where you can, and invest in sophistication only where it moves the needle. 
• Comfortable with ambiguity, reorganizations, and shifting priorities. 
• High ownership. If it is broken and it touches an employee, you treat it as yours, even if the fix lives in someone else's system. 

About The Role

In this role, you will be the security czar for the Cerebras’s AI cluster product. Such AI clusters have 100’s of Wafer-scale accelerator systems, 1000’s of high-end servers, and several 1000’s of networking ports including switches. Plus, there will be network attached storage, all in a large-scale datacenter.

You will ensure that Cerebras’s large-scale AI clusters are secured through first-principles, best practices, security-first based engineering. Cerebras cluster involves complex HW components, networking and a vertically integrated cluster management software stack – all the way from a bare-metal deployment that brings up an operational cluster to a suite of cluster management software that enables multi-tenant higher-level training and inference services to be hosted on such large clusters.

Your role will be to ensure both end-to-end security as well as privacy of such cluster use-cases. You will develop security engineering solutions that have the necessary network access control, user access controls, and world-class multi-tenancy solution

Responsibilities

• Be the primary engineering face and owner of cluster security.
• Provide strong technical leadership in cluster security for the company.
• Actively work with corporate security, and customers to identify and define security enhancements needed.
• Build engineering driven software that will provide guardrails, detection solution and response tools for vulnerabilities at all layers of vertical stack (includes HW and SW).
• Straddle vertically and horizontally cross functional collaboration to ensure end-to-end cluster software is secure.
• Develop, maintain and execute roadmap of the cluster security product.
• Build an outstanding engineering team to deliver world-class security solution.

Skills & Qualifications

• 3+ years of demonstrated engineering leadership/management role in distributed systems security.
• Proven track record of delivering product, launching and deploying secured distributed solutions to customers.
• Excellent communication, articulation, collaboration and ability to act as a stakeholder.
• Tough decision-making skills with data and trade-off analysis.
• Outstanding sense for product and user journeys, out-of-box thinker.
• Outstanding road map and schedule execution skills under tight timeline and budgets.
• Strong background in multi-tenancy of large scale clusters is necessary.
• Strong technical experience in computer and cluster networks is necessary.
• Strong technical background in distributed systems software development (K8s and its ecosystem) is preferred.
• Technical experience with bare metal cluster management software and related monitoring is preferred.

The salary range for this position is $140,000 - $240,000 annually. Actual compensation will be determined based on factors such as experience, skills, qualifications, and location.

Advanced Packaging Technologist & Lead 

We are seeking an accomplished Advanced Packaging Technologist & Lead to drive the development, integration, and deployment of next‑generation semiconductor packaging technologies. This role is critical in architecting and implementing advanced, high‑performance, and high‑density packaging solutions supporting cutting‑edge compute, AI, and heterogeneous integration platforms. 

Key Responsibilities 

Advanced Packaging Architecture & Development 

• Design and implement advanced semiconductor packaging technologies, including 2.5D/3D stacking, heterogeneous integration, high-bandwidth interconnects, and advanced power-delivery architectures. 

• Lead R&D in Chip-on-Wafer (CoW) and Wafer-to-Wafer (W2W) bonding approaches for high-density integration. 

• Develop and optimize solutions using silicon interposers, Through-Silicon Vias (TSVs), and multi‑layer RDL packaging to enable ultra‑high‑bandwidth and low‑latency connections. 

• Engineer advanced packaging structures using low‑CTE substrates, FLEX interconnects, and organic or ceramic substrate technologies. 

• Align internal architects and external partners to deliver manufacturable designs and steer our strategic technology direction. 

• Leverage simulation-driven design to reduce hardware iteration cycles and ensure first-pass success in complex architectures. 

Assembly, Materials, & Interconnect Technologies 

• Drive technology innovation in advanced buildup substrates, including designs with and without embedded dies. 

• Oversee flip-chip bonding processes using both solder balls and copper pillars. 

• Lead development of substrate embedding for silicon dies, capacitors, passives, and other active components. 

• Develop and refine advanced dicing methodologies (laser and mechanical saw) tailored for nanometer-class nodes. 

• Select materials; solder alloys, underfills, thermal interface materials (TIMs), and other key materials that enable high performance, manufacturability, and reliability 

Process Technology & Reliability 

• Manage package-level and board-level qualification, ensuring robust performance across thermal, mechanical, and electrical stress conditions 

• Lead analysis and improvements in solder reliability, including temperature cycling, electromigration (EM), and stress modeling. 

• Oversee ultra-thin die handling and processing for fragile, high-performance devices. 

• Drive backside metallization and RDL process development to support advanced packaging roadmaps. 

• Lead failure analysis when a new design fails a stress test and pivot the team toward a solution. 

Qualifications 

• BS EE, MS EE or equivalent engineering discipline 

• 10+ years of experience in advanced packaging 

• Highly preferred: working knowledge of simulation tools (i.e. Ansys, Cadence, Abaqus) 

 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 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.

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 

We are building a high-performance SRE function to support one of the world’s fastest-growing AI inference services, powered by the Wafer-Scale Engine (WSE). This team will help deliver world-class, ultra-reliable inference infrastructure for leading model builders such as OpenAI and other frontier labs. 

As a Staff SRE, you will lead the engineering effort to eliminate toil at scale by driving implementation of self-service delivery pipelines, shared observability common tooling. This role starts with ~1 month of hands-on operational immersion to gain deep familiarity with our current stack, production pain points, and high-stakes workflows.  

From there, your primary focus shifts to architecting and delivering the "tomorrow" layer: declarative GitOps-driven CD for model releases, capacity provisioning and cluster upgrades. Success over the first year in this role will be defined by enabling core teams, product managers, external customers, and cluster stakeholders to operate in a fully self-service model with strong reliability guarantees. 

You will partner with our early-career SRE sub-team, who own day-to-day operations. This will allow you to deeply understand their pain points, automate their toil, and mentor them as platform engineers.  

You will collaborate with the tech leads and the leadership team across core, cluster, cloud, and product stakeholders. This work will shift reliability from an ops-only burden to a shared engineering discipline that underpins frontier AI inference at scale. 

If you are a proven Staff+ engineer who enjoys turning complexity into elegant reliability at scale, this is your chance to lead this transformation from the front. 

This role does not require 24/7 on-call rotations. 

Key Responsibilities 

• Define and implement a robust strategy for delivering and running software reliably and at scale across multiple datacenters and cloud-based solutions. 

• Architect self-service platforms and internal tooling that let product teams, external customers, and cluster operators safely trigger and observe critical workflows with minimal handoffs.  

• Define and evolve reliability practices for inference workloads, including SLOs and SLIs for latency, throughput, and accuracy stability; error budgets; blameless postmortems; chaos testing; and capacity forecasting across multi-datacenter and on-prem environments.  

• Mentor mid-level SREs, support critical incident escalations, and use production pain points to prioritize the highest-leverage automation work.  

• Measure and drive impact through clear metrics, including toil reduction, deployment velocity, SLO compliance, MTTR, and adoption of self-service workflows. 

Required Experience & Skills 

• 8+ years in SRE, infrastructure engineering, or platform engineering, with a strong record of improving automation and reliability at large scale in FAANG, hyperscaler, or similarly demanding environments.  

• Deep expertise operating large scale heterogenous clusters with a proprietary cloud control plane 

• Proven track record designing and delivering CI/CD or GitOps systems using Argo CD or similar tools, with strong safety and observability built in.  

• Hands-on experience with observability systems such as Loki, Tempo, Mimir, and Prometheus  

• Ability to lead complex projects end to end, influence cross-functional stakeholders, and communicate technical direction clearly. 

Nice-to-Haves 

• Experience with Bazel or other large-scale build systems in production.  

• Background in AI/ML inference systems, including model serving runtimes, GPU or wafer-scale orchestration, latency and accuracy SLOs, or drift monitoring.  

• Prior work on predictive autoscaling, chaos engineering, or cost-aware capacity planning for compute-intensive workloads.  

Location   

• SF Bay Area 

• Toronto 

About The Role

We're looking for a deeply technical, hands-on software engineer to join our on-field Kernel Reliability team. You'll help tackle a critical challenge: improving the reliability of our advanced compute clusters and the underlying inference, training, and internal production services. In this role, you'll work close to the code and design solutions that will scale with our rapidly growing system production and software service offerings. If you have strong fundamentals in systems, debugging, and failure analysis—and enjoy building tools and solving hard reliability problems—we want to hear from you. New college graduates are welcome.

Responsibilities

• Contribute to the technical roadmap and execution for kernel-centric reliability of our internal and customer-facing systems.
• Partner with System and Cluster Operations teams to reduce system and service downtime after failure through tooling, analysis, and hands-on debugging support.
• Work with the Debug Team to enhance debug tools with the goal of speeding up failure analysis.
• Collaborate with software teams to improve the software stack—including kernels—to improve on-field debugging and failure analysis.
• Work with ASIC and hardware architecture teams to co-design next-generation architectures with reliability and ease of debug in mind.
• Participate in incident response, root-cause analysis, and post-mortems; drive follow-ups that measurably improve reliability over time.

Skills & Qualifications

• We recognize great engineers come from different backgrounds. If you're excited about the role, we encourage you to apply even if you don't meet every qualification.
• Required (or demonstrated through projects/internships/coursework):
  • Strong programming skills in C/C++ and Python.
  • Solid foundations in operating systems, computer architecture, and systems programming fundamentals.
  • Ability to debug complex issues using logs, traces, and standard debugging workflows; interest in root-cause analysis.

Nice to have:

• Exposure to parallel and distributed programming (message passing, multicore, GPU, embedded, etc.).
• Experience building or using debug/diagnostic tools (debuggers, core dump handling, tracing, sanitizers, profilers, etc.).
• Familiarity with debugging distributed and parallel applications (deadlocks, livelocks, race conditions, etc.).
• Knowledge of computer architecture concepts (instruction pipelining, multithreading, networking, memory systems, etc.).
• Operations & Monitoring: familiarity with monitoring, incident response, and post-mortem culture.

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. 

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

The Cybersecurity GRC Manager is accountable for maturing and scaling engineering-driven governance, risk, and compliance programs that support the security, privacy, and regulatory-compliant posture of the organization. The ideal candidate will bring a unique blend of deep technical security acumen and GRC expertise, enabling the creation of GRC workflows that are measurable, automated, and resilient. This is a strategic, cross-functional, and customer-facing role reporting to the Director of Governance, Risk, & Compliance. 

A successful candidate will have a comprehensive understanding of cybersecurity and privacy industry frameworks (e.g., NIST, ISO, SOC 2, CCPA, GDPR, HIPAA). They will be responsible for transforming governance, risk, and compliance practices into proactive, testable capabilities using automation, continuous auditing, and AI-driven solutions. 

Proficiency with AI tools (LLMs, prompt engineering, generative‑AI workflows) is a core requirement – you’ll use AI to streamline GRC workflow creation and implementation, evidence generation, and security risk mitigation. Experience with designing and implementing autonomous “agentic AI” solutions is preferred. 

Responsibilities 

• Drive a compliance operating model that includes automated control testing, self-service reporting, and AI-enhanced risk analysis. Implement continuous control monitoring and evidence collection pipelines integrated into cloud-native and on-prem environments. 
• Partner with engineering and product teams to define and codify security and compliance requirements as part of the SDLC. Introduce automated security/compliance tests into CI/CD pipelines to support shift-left practices. 
• Use generative AI for compliance gap detection, policy mapping, risk triaging, and customer assurance functions. 
• Oversee security and privacy assurance activities and assessments, internal/external audits, and attestation/certification initiatives (e.g., SOC 2, ISO 27001). Lead internal readiness for third-party audits and external assessments and maintain ongoing compliance posture. 
• Utilize automation and GRC platforms to optimize gathering and maintenance of audit readiness documentation and audit evidence. 
• Utilize AI-driven solutions to manage the organization’s responses to customers’ and partners’ cybersecurity requests (e.g. information security questionnaires). 
• Enhance and execute third-party security risk management practices, including inherent / residual security risk assessment, vendor / supplier security due diligence reviews, vendor / supplier inventory management, ongoing security monitoring, and risk reporting. 
• Build and maintain enterprise-level risk registers; facilitate and monitor security risk acceptance processes; design and maintain security risk measurement and monitoring including risk reporting. 
• Grow and expand cybersecurity guidance through development and maintenance of cybersecurity policies, standards, and procedures. 
• Manage security awareness programs through administration of regular security trainings, phishing simulations, and corporate communications. 

Skills And Qualifications   

Required Experience 

• Bachelor’s degree in computer science, Cybersecurity, or related engineering field; advanced degree preferred. 
• Minimum 5 years of progressive experience in cybersecurity, security engineering, and/or risk management. 
• Proven success managing compliance programs in cloud-native, SaaS/PaaS environments with high automation maturity. 
• Demonstrated ability to manage customer-facing compliance engagements and audit preparation. 

Technical and Domain Expertise 

• Deep knowledge of, and experience working with, industry frameworks (NIST SP800-53, ISO 27001, SOC 2, CCPA, GDPR, HIPAA).  
• Strong familiarity with AI/ML usage in security programs and risk analysis.  
• Experience implementing and administering GRC tools/platforms. 
• Proficiency in cloud security, AI security, secure development / DevSecOps practices, and infrastructure-as-code (IaC) security tooling. 
• Experience implementing automated compliance and control validation pipelines. 

Soft Skills 

• Excellent communication, stakeholder management, and executive reporting skills. 
• Ability to influence cross-functional teams and operate in fast-paced, high-growth environments. 
• Strong analytical, critical thinking, and decision-making capabilities. 

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.

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.

Own the Future of AI Inference

Cerebras powers the world's fastest AI inference. As the Product Manager for AI Models, you'll lead the strategic model portfolio that defines our product — deciding which models ship, how they perform, and how the world discovers them.

You'll partner directly with leading AI labs, drive launches that shape the industry, and ensure every model on our platform delivers exceptional quality at unprecedented speed.

What You'll Own

Strategic Model Portfolio

• Own the models roadmap: decide which frontier and open-source models we support based on market demand, research trends, and strategic fit
• Establish partnerships with top model labs, for day0 launches
• Build relationships with open-source maintainers to accelerate community model adoption

Product Quality & Customer Success

• Define and enforce quality standards across our model catalog through systematic evaluation frameworks
• Design benchmarks and evaluations that prove our models deliver production-grade performance
• Own the feedback loop: gather customer insights, identify model weaknesses, and drive improvements with engineering
• Enable strategic customers to integrate our inference into their products—removing blockers and optimizing for their specific use cases

Go-to-Market Excellence

• Lead high-impact model launches that generate buzz and adoption
• Create compelling product marketing: demos, benchmarks, tutorials, and documentation that showcase what's possible on Cerebras
• Craft technical content that resonates with developers and decision-makers alike

Technical Decision-Making

• Select and prioritize performance optimizations (quantization, speculative decoding, etc.) based on customer needs and hardware capabilities
• Collaborate with optimization engineers to implement techniques that maximize our speed advantage
• Balance tradeoffs between quality, latency, throughput, and cost

Cross-Functional Leadership

• Orchestrate launches across model enablement, optimization engineering, deployment, sales, and marketing
• Drive alignment in a fast-moving environment where priorities shift based on model releases and customer needs
• Be the voice of the customer to engineering and the voice of product to customers

Skills & Qualifications  

What we need to see:

1. 5+ years of experience as a product manager, currently at or above the level of Senior PM.
2. 5+ years of total technical work experience (e.g. SWE, ML researcher, solution engineer).
3. Ability to thrive in a fast-paced, dynamic environment. With an entrepreneurial sense of ownership and ability to lead projects.
4. Knowledge and passion for the worlds of open-source models and generative AI research.
5. Knowledge of the community model ecosystem, including: PyTorch, Hugging Face, vLLM, and SGLang.
6. Highly motivated, independent, organized, and an effective communicator.
7. Comfortable using Python with the chat completions API, for basic model testing.

Preferred requirements  

How to stand out:

1. Product manager experience at a model training lab or a company that implements open-source models.
2. Experience working with customers in a solution engineering role.
3. Experience writing technical marketing assets and social media, with a growing portfolio.
4. Experience working in a cross-functional organization, and leading projects across multiple teams.
5. Experience writing model quality evaluations and system prompt harnesses.
6. Experience writing application code in use cases such as code generation or deep research search application.
7. Expertise on agentic flows and current LLM model family architectures.
8. Understanding of model compilers and optimization.
9. Contributor to communities like vLLM, SGLang, PyTorch, or Hugging Face transformers.
10. Experience with model optimization or compression methods like quantization.

Location   

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

Responsibilities 

• Lead printed circuit board design through all development stages: from definition to implementation, bring-up, qualification, and production release. 

• Full responsibility for electrical specification, schematic design, components selection, and layout considerations. 

• Extensive lab bring-up and debugging, including developing automated benchtop setups for board characterization. 

• Collaborate with various design and operations teams: manufacturing operations & test engineering, supply chain, ASIC, mechanical, signal integrity, power delivery, layout, embedded & diagnostic, etc. 

Skills & Qualifications 

• B.S.c, M.S.c, or Ph.D. degree in electrical engineering, or equivalent experience. 

• 5 to 10 years of experience as a board design engineer (exceptional candidates with other experience levels will also be considered). 

• Experience with digital circuits, power delivery, voltage regulators, and high-speed signal integrity. 

• System-level understanding and familiarity with production environment. 

• Proficiency with lab/test scripting SW (e.g., Python). 

• Personal skills: self-instruction, multi-disciplined, multi-tasking and good interpersonal relationship. 

The base salary range for this position is $150,000 to $260,000 annually.  Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.

Location: Sunnyvale, California

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.

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.

About The Role

The Inference ML Engineering team at Cerebras Systems is dedicated to enabling our fast generative inference solution through simple APIs powered by a distributed runtime that runs on large clusters of our own hardware. Our mission is to empower enterprises, developers, and researchers to unlock the full potential of our platform, leveraging its performance, scalability, and flexibility. The team works closely with cross-functional groups, including compiler developers, cluster orchestrators, ML scientists, cloud architects, and product teams, to deliver high-impact solutions that redefine the boundaries of ML performance and usability.

As a Senior Software Engineer on the Inference ML Engineering team, you will play a key role in designing and implementing APIs, ML features, and tools that enable running state-of-the-art generative AI models on our custom hardware. You will architect solutions that enable seamless model translation and execution, ensuring high throughput and low latency, while maintaining ease of use. Your responsibilities will include leading technical initiatives, collaborating with other engineering teams to enhance the developer experience, enabling key ML features at scale, maintaining our speed advantage, achieving high throughput, and supporting a wide range of ML workloads. This role offers an opportunity to shape the evolution of our ML ecosystem while tackling complex technical challenges at the intersection of machine learning, software, and hardware.

Responsibilities

• Drive and provide technical guidance to a team of software engineers working on complex machine learning integration projects.
• Design and implement ML features (e.g., structured outputs, biased sampling, predicted outputs) that improve performance of generative AI models at inference time.
• Design and implement high-throughput, low-latency multimodal inference models that support delivery of image, audio, and video inputs and outputs.
• Maintain our scalable serving backend for handling many concurrent requests per minute.
• Scale our inference service by implementing detailed observability throughout the entire stack.
• Analyze and improve latency, throughput, memory usage, and compute efficiency on the service and the implementation of various features.
• Optimize software to accelerate generative LLM inference by achieving high throughput and low latency.
• Stay up-to-date with advancements in machine learning and deep learning, and apply state-of-the-art techniques to enhance our solutions.
• Evaluate trade-offs between different approaches, clearly articulate design choices, and develop detailed proposals for implementing new features.
• Uncover, scope, and prioritize significant areas of technical debt across the software stack to ensure continued high quality of the inference service.
• Build and maintain robust automated test suites to ensure software quality, performance, and reliability.
• Contribute to an agile team environment by delivering high-quality software and adhering to agile development practices.
• Lead cross-functional initiative across the company to deliver high-quality inference solutions.

Skills and Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Mathematics, or a related field.
• 8+ years of experience in large-scale software engineering, with a focus on deep learning or related domains.
• Proficiency in Python for building and maintaining scalable systems.
• Advanced proficiency in C++, with an emphasis on multi-threaded programming, performance optimization, and system-level development.
• Demonstrated experience driving cross-functional projects.
• Experience building and scaling large-scale inference systems for LLMs or multimodal models.
• Familiarity with LLM serving frameworks, such as vLLM, SGLang, and TensorRT-LLM.
• Solid understanding of software architectural patterns for large-scale, high-performance applications.
• Hands-on experience with ML frameworks, such as PyTorch, and a strong understanding of their underlying architectures.
• Strong problem-solving skills, with the ability to balance technical depth with practical implementation constraints.
• Exceptional communication and presentation skills, with the ability to work both independently and collaboratively across multidisciplinary teams.

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.   

Location: Sunnyvale 

We're hiring a Staff Engineer to own major areas of the architecture of our Inference Cloud Platform. This team owns the cloud layer behind our Inference Service, with responsibility for availability, latency, reliability, and global scale. 

This is a hands on IC role for an engineer who wants to work on the hardest distributed systems problems in the stack: multi-region traffic architecture, graceful degradation under bursty AI workloads, performance at high QPS, and the operating model for a platform that has to stay fast and available under load. You'll write code, lead key architectural decisions in your domain, debug production issues, and help shape technical direction across adjacent teams. 

If you're interested in building the next-generation architecture of a globally distributed inference platform, we'd like to talk. 

Responsibilities 

• Platform Direction. Help shape the technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas. 

• Core Cloud Systems. Design and build critical platform components such as service discovery, request routing, load balancing, caching, batching, and traffic management for AI inference workloads. 

• Reliability & Performance. Architect active-active systems with rapid failover, graceful degradation, and clear SLOs. Drive system-level improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand. 

• Traffic Control & Service Tiers. Define platform mechanisms for admission control, quota management, rate limiting, and differentiated quality of service across workload types and customer tiers. 

• Execution on Critical Paths. Write and review production code in the most important parts of the platform. Make high-consequence architectural decisions within your area and set the technical bar through design reviews, code reviews, and sound engineering judgment. 

• 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 Influence. Partner with ML, Product, Infrastructure, and Platform teams to translate product and business requirements into scalable system designs, and drive alignment on shared technical decisions within your domain and adjacent platform surfaces. 

• Mentorship. Raise the effectiveness of senior engineers through design feedback, pairing, and clear technical standards. 

Skills & Qualifications 

• 8+ 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. 

• 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 SLO-driven operations. 

• Ability to influence senior engineers and cross-functional partners through technical credibility, communication, and judgment, especially within your domain and adjacent systems. 

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

About The Role

As a frontend engineer on our AI cloud, you will work on our customer-facing inference, training, and admin consoles and API experiences. In this role, you will be responsible for designing and building responsive, user-friendly frontend interfaces that provide an optimal experience for our developers, handling high traffic and throughput efficiently. 

Your familiarity with the latest web development frameworks and best practices, coupled with a keen eye for design and user experience, will drive team success. 

We're looking for talented software engineers who thrive in ambiguity, view change as an opportunity, and have a voracious desire to learn and share knowledge clearly and concisely. 

Skills And Qualifications   

• 5+ years as an individual contributor developing production-grade web frontend products and experiences. 
• Deep experience with web technologies such as NextJS, React, and TailwindCSS. 
• Familiarity with backend technologies such as Prisma, PostgreSQL, and Auth.js. 
• Experience with building high-reliability, production-grade cloud solutions. 
• Familiarity with architecting and developing advanced web applications. 
• Strong problem-solving skills and the ability to thrive in ambiguous, rapidly changing conditions. 
• Excellent communication skills and a passion for continuous learning and knowledge sharing.