About The Role

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

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

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

Scope of the team includes:

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

Skills & Qualifications

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

Preferred Skills & Qualifications

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

Location

• On-site or hybrid at our Toronto office

#LI-WA1

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'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

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

Responsibilities

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

Requirements

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

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

Responsibilities:

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

Skills & Qualifications:

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

Preferred:

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

Location:

Sunnyvale, California.

Bangalore, India

Toronto, Canada 

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

About The Role 

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

Key Responsibilities: 

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

Requirements: 

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

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

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

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

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
• Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc.
• Strong debugging skills across performance, numerical accuracy, and runtime integration.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion).
• Proficiency in C/C++ programming and experience with low-level optimization.
• Proven experience in compiler development, particularly with LLVM and/or MLIR.
• Strong background in optimization techniques, particularly those involving NP-hard problems.

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

About The Role

About The Role

As an Applied Machine Learning Research Scientist at Cerebras, you will play a key role in turning modern machine learning techniques into scalable, high-performance systems. This role sits at the intersection of modeling and systems focused not on publishing new algorithms, but on understanding how they work and making them run effectively at scale. Your work will directly impact how large language models (LLMs) are trained, optimized, and deployed on one of the most advanced AI platforms in the world. 

You will work closely with researchers and senior engineers to implement and improve workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. This includes building training pipelines, debugging complex system behaviors, improving model quality, and iterating on data and evaluation strategies. Your contributions will help translate cutting-edge ML ideas into reliable, production-ready systems that solve real-world problems. 

This role is ideal for candidates who enjoy hands-on engineering, want to build deep intuition for ML systems, and are excited about working on LLMs and reinforcement learning in practice, not just in theory. 

Responsibilities 

• Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance.
• Build and maintain evaluation pipelines to measure model performance across tasks and domains.
• Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation.
• Collaborate with researchers to translate ML ideas into efficient, scalable implementation.
• Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment).
• Work with large datasets, including dataset generation, filtering, and synthetic data approaches.
• Optimize training and inference workflows for performance, efficiency, and reliability.
• Contribute high-quality, maintainable code to shared ML infrastructure. 

Skills & Qualifications    

•  Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 0 - 5 years of experience (including internships, research, or industry experience) working with machine learning systems; we are hiring multiple positions for various levels. 
• Strong programming skills in Python.
• Experience with ML frameworks such as PyTorch.
• Solid understanding of machine learning fundamentals.
• Familiarity with deep learning architectures, particularly transformers.
• Ability to read and understand modern ML papers and implement key ideas.

Preferred Skills & Qualifications 

• Experience working with large language models (training, fine-tuning, and evaluation).
• Familiarity with reinforcement learning concepts.
• Experience with distributed training frameworks (e.g., FSDP, Megatron).
• Experience working with large-scale datasets and data pipelines.
• Experience debugging or optimizing ML systems for performance.
  • Contributions to meaningful codebases, projects, or open-source systems 

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

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

About the Role

In this role, you will be building a world class UI-based large-scale cluster management portal. This portal will act as one stop for all operations and maintenance of cerebras clusters – such as cluster bringup deployment (day0/1/2), job management, health management to name a few. Cerebras AI clusters may have 1000’s of

Wafer-scale accelerator systems, several 1000’s of high-end servers, and several 1000’s of networking ports including switches.

Responsibilities

• Be the primary engineering face and owner of UI and integrating to the backend through standard best practices.
• Heavily partner with product management and end users of this tool to build a world class tool.
• Provide strong technical leadership for this tool development.
• Actively work with variety of engineering teams that needs interaction in backend.
• Build UI experience that is cohesive and seamless across all operations and maintenance activities.
• Ability to build and mentor a small team of engineers for this tool.

Skills & Qualifications

• 6+ years of demonstrated technical excellence in UI development and backend integration.
• 5+ years of professional software engineering experience with modern front-end frameworks such as React, Angular, or Vue.
• 5+ years of technical engineering experience with coding in languages including, but not limited to, C++, TypeScript, JavaScript, or Python.
• 2+ years of back-end development experience using technologies like Node.js, Python or Go with a proven track record of designing scalable APIs and microservices.
• Expertise in HTML, CSS, JavaScript/TypeScript, and responsive design principles to deliver polished, accessible, and high-performance user interfaces.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience in CI/CD pipelines for deploying and maintaining production-grade applications.
• Proven track record of delivering product, launching and deploying solutions in production.
• Excellent communication, articulation, collaboration and stakeholder management.
• 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.

About The Role

Join the inference model team dedicated to bring up the state-of-the-art models, numerically validating and accelerating new model ideas on wafer-scale hardware. You will prototype architectural tweaks, build performance-eval pipelines, and turn hard numbers into changes that land in production.

Key Responsibilities

• Prototype and benchmark cutting-edge ideas: new attentions, MoE, speculative decoding, and many more innovations as they emerge. 
• Develop agent-driven automation that designs experiments, schedules runs, triages regressions, and drafts pull-requests. 
• Work closely with compiler, runtime, and silicon teams: unique opportunity to experience the full stack of software/hardware innovation. 
• Keep pace with the latest open- and closed-source models; run them first on wafer scale to expose new optimization opportunities. 

Skills And Qualifications 

• 3 + years building high-performance ML or systems software. 
• Solid grounding in Transformer math—attention scaling, KV-cache, quantisation—or clear evidence you learn this material rapidly. 
• Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc. 
• Strong debugging skills across performance, numerical accuracy, and runtime integration. 
• Prior experience in modeling, compilers or crafting benchmarks or performance studies; not just black-box QA tests. 
• Strong passion to leverage AI agents or workflow orchestration tools to boost personal productivity.

Assets

• Hands-on with flash-attention, Triton kernels, linear-attention, or sparsity research.
• Performance-tuning experience on custom silicon, GPUs, or FPGAs. 
• Proficiency in C/C++ programming and experience with low-level optimization. 
• Proven experience in compiler development, particularly with LLVM and/or MLIR. 
• Publications, repos, or blog posts dissecting model speed-ups. 
• Contributions to open-source agent frameworks.

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.

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. 

About the Role

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

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

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

Responsibilities

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

Minimum Skills & Qualifications

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

Preferred Skills

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

Location

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

• Provide hands-on technical leadership, owning the technical vision and roadmap for the kernel-centric reliability of our internal and customer-facing systems
• Assist System and Cluster Operations teams on reducing system and service downtime after failure by providing tooling and manual intervention for failure analysis and diagnostic
• Work with the Debug Team to enhance debug tools with the goal of speeding up failure analysis
• Collaborate with SW teams to improve the software stack, including Kernels, to improve on-field debugging and failure analysis
• Work with the ASIC an HW architecture teams to codesign the next generation architectures with reliability and ease of debug in mind
• Lead, mentor, and grow a high-caliber team of engineers, fostering a culture of technical excellence and rapid execution. 

• 6+ years in software engineering, with 3+ years leading teams in SW/HW reliability, debug, diagnostic, failure analysis or related fields 
• Expertise in parallel and distributed programming (message passing, multicore, GPU, embeded, etc.), debug and diagnostic tool development or expert usage (debuggers, core dump handling, code sanitizers, etc.), experience debugging distributed and parallel applications (deadlocks, livelocks, race conditions, etc.), deep understanding of computer architectures (instruction pipelining, multithreading, networking, etc.)
• Operations & Monitoring: Strong background in monitoring and reliability engineering (incident response, post-mortem analysis, etc.)
• Leadership & Collaboration: Demonstrated ability to recruit and retain high-performing teams, mentor engineers, and partner cross-functionally to deliver customer-facing products.

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. 

About The Role

We’re hiring a staff level full-stack Technical Lead (L6/L7) to own and scale critical parts of the Cerebras Developer Console — the primary interface developers and enterprises use to run and manage inference workloads.

This is a deeply technical, end-to-end role. You’ll build high-quality frontend systems (Next.js, TypeScript) and design backend services (GraphQL, Postgres, Redis) that power usage tracking, billing, quotas, and observability. The systems you build will operate at high scale, require careful data modeling, and balance real-time and batch processing. You’ll be expected to make strong architectural decisions and move quickly from idea to production.

You’ll join an existing, high-velocity team and take ownership of major platform areas such as billing, request logs, and metrics. This is not a “ticket execution” role — you’ll define problems, drive technical direction, and lead execution across the stack. The work directly impacts customer experience and revenue, and the expectations are correspondingly high.

As a Technical Lead, you’ll set the bar for engineering quality and execution. You’ll mentor engineers, drive design reviews, and push the team toward simple, scalable solutions. We’re looking for someone who thrives in fast-moving environments, operates with urgency, and is comfortable navigating ambiguity while shipping high-quality systems.

What You’ll Do

• Own a major area of the platform — take end-to-end responsibility for systems such as billing, usage tracking, quotas, request logs, or metrics.
• Build and evolve core systems — design and implement APIs, services, and UI that power the Developer Console and scale with growing customer usage.
• Make architectural decisions — define system boundaries, data models, and tradeoffs across real-time vs batch processing, performance, and cost.
• Drive projects from 0 → 1 → scale — take ambiguous problems, define solutions, and deliver them to production.
• Lead technical execution — break down work, align engineers, and ensure high-quality delivery across the stack.
• Improve system reliability and visibility — ensure systems are observable, debuggable, and production-ready.
• Partner with product and design — shape developer-facing workflows and experiences.

What We’re Looking For

• Track record of ownership — you’ve led and delivered complex systems end-to-end, not just contributed components.
• Technical depth in backend systems — strong fundamentals in APIs, data modeling, and distributed systems; experience with high-scale or real-time systems is a plus.
• Full-stack capability — comfortable working across frontend and backend, with the ability to make pragmatic tradeoffs.
• Strong technical judgment — you make sound architectural decisions and know when to optimize vs move fast.
• Ability to operate without structure — you bring clarity to ambiguous problems and drive execution independently.
• High standards for quality — you care about correctness, maintainability, and long-term system health.
• Influence and mentorship — you elevate the team through design reviews, guidance, and leading by example.
• Bias for action — you move quickly, unblock yourself and others, and focus on impact.
• Experience in fast-moving environments — comfortable with shifting priorities and evolving scope.
• Experience — typically 8+ years of industry experience building and operating production systems.
• Education — Bachelor’s or master's in computer science (or equivalent practical experience).

Why Cerebras

Cerebras is redefining the speed and scale of AI inference. The systems we build power real-world production workloads, not demos.

• Work on systems that matter — you’ll build core platform infrastructure that directly impacts how customers run and scale AI workloads in production.
• Own meaningful surface area — this is not a narrow role. You’ll take ownership of critical systems (billing, usage, observability) that sit at the heart of the platform.
• Operate at the intersection of product and infrastructure — the work spans developer experience, distributed systems, and real-time data, requiring both strong engineering and product thinking.
• High impact, low bureaucracy — you’ll work in a small, high-performing team where decisions are made quickly and engineers have real influence on direction.
• Grow with the platform — as we expand from cloud offerings into broader infrastructure and cluster management, the scope and technical challenges will continue to grow.

About The Role

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

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

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

Responsibilities

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

Skills & Qualifications

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

Preferred Skills & Qualifications

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

Location: Sunnyvale 

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

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

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

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

Responsibilities 

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

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

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

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

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

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

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

Skills & Qualifications 

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

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

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

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

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

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

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

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

About The 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 Inference ML Engineering team at Cerebras builds the runtime, APIs, and systems that power the fastest generative AI inference platform in the world.

As an Engineering Manager, Inference ML Runtime, you will lead a team responsible for designing and scaling the systems that enable seamless execution of state-of-the-art AI models on Cerebras hardware. You will operate at the intersection of machine learning, distributed systems, and high-performance runtime engineering, translating cutting-edge research into production-ready infrastructure to serve a variety of text-only and multimodal models.

This role combines technical leadership, people management, and execution ownership, with direct impact on Cerebras’ core inference platform.

What You’ll Do

Technical Leadership

• Own the architecture and evolution of the ML inference runtime and serving systems.
• Guide the design of:
• high-throughput, low-latency inference pipelines;
• multimodal model execution (text, image, audio, video);
• scalable serving infrastructure for concurrent workloads.
• Partner with cloud, compiler, core runtime, hardware, and ML teams to optimize end-to-end performance.

Team Leadership

• Build, manage, and grow a team of ML systems and infrastructure engineers.
• Provide technical direction, mentorship, and career development.
• Foster a culture of ownership, velocity, and engineering excellence.
• Recruit top talent in ML systems, distributed systems, and runtime engineering.

Execution & Delivery

• Drive execution of complex, cross-functional initiatives across:
• ML engineering;
• compiler/runtime teams;
• cloud and infrastructure teams.
• Own delivery of features such as:
• advanced inference capabilities (structured outputs, sampling strategies);
• heterogeneous model types, including test and multimodal;
• performance optimization (latency, throughput, memory efficiency);
• observability and reliability across the inference stack.
• Ensure high-quality releases through strong testing, validation, and operational rigor.

Platform & Performance Ownership

• Scale Cerebras’ inference platform to handle large volumes of concurrent requests at very fast speed
• Drive improvements in:
• latency;
• throughput;
• compute efficiency.
• Identify and prioritize technical debt and system bottlenecks.
• Maintain Cerebras’ industry-leading inference speed advantage.

Cross-Functional Collaboration

• Partner with:
• ML researchers (model enablement);
• compiler teams (model execution optimization);
• cloud/platform teams (deployment and scaling).
• Act as a bridge between research, infrastructure, and production systems.

What You Bring

Required

• 8+ years of experience in:
• large-scale software engineering;
• ML systems or distributed systems.
• 2+ years of engineering management experience.
• Strong programming skills in:
• Python (production systems);
• C++ (performance-critical systems).
• Experience building and scaling large-scale inference systems (LLMs or multimodal).
• Experience working with cloud infrastructures and following best-practices for building scalable microservices and applications.

Preferred

• Experience with:
• LLM serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang);
• PyTorch and deep learning frameworks;
• distributed systems and high-performance computing.
• Familiarity with:
• ML runtime systems;
• model execution pipelines;
• performance optimization for AI workloads.

Why This Role Matters

This team is central to Cerebras’ mission of delivering the fastest AI inference in the world. Your work will directly enable real-time AI applications and unlock new capabilities across enterprise and frontier AI use cases.

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.

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), 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 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