Research Scientist role focused on designing AI models and training methods from first principles, leveraging novel wafer-scale hardware architectures. The role involves investigating computational science techniques for AI, understanding hardware-algorithm interactions, and publishing research at top-tier venues. The work directly influences future hardware and software design.

Research Engineer focused on adapting and optimizing advanced language and vision models for efficient inference on Cerebras' wafer-scale AI architecture. The role involves implementing, validating, and optimizing models for low-latency, high-throughput inference, with a focus on techniques like speculative decoding, pruning, compression, and sparsity.

Research Engineer role focused on designing and implementing AI/ML workloads on Cerebras' wafer-scale hardware, optimizing performance, and contributing to future hardware/software roadmaps. Involves algorithm-hardware co-design, performance modeling, and publishing research.

This role focuses on applying and scaling modern machine learning techniques, particularly LLM post-training (RLHF, GRPO), on Cerebras' wafer-scale AI chip. The scientist will build and maintain training pipelines, evaluation frameworks, and optimize ML workflows across pretraining, fine-tuning, and alignment stages, working with large datasets and contributing to shared ML infrastructure.

The Kernel Engineer will develop high-performance software solutions for AI and HPC workloads, focusing on implementing, optimizing, and scaling deep learning operations on Cerebras' custom hardware. This involves designing, developing, and debugging low-level kernels and algorithms to maximize compute utilization and training efficiency, while also studying emerging ML trends and interacting with hardware architects.

Senior ML Systems Engineer to join the SOTA Training Platform team, responsible for bringing up state-of-the-art open-source and proprietary ML models on Cerebras CSX systems. This role involves working across the full stack, including model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning, with a focus on debugging and improving the bring-up process.

Applied AI Scientist role focused on developing and customizing large language and deep learning models for customer problems using Cerebras' wafer-scale engine. Responsibilities include customer use case discovery, architecting and executing end-to-end training recipes, fine-tuning models, building agentic system components, and providing technical customer leadership. Requires strong expertise in deep learning, large model training/fine-tuning, Python, PyTorch, and distributed training.

Cerebras is seeking a Principal ML Investigator to lead a new ML team focused on advanced development in areas like post-training, reinforcement learning, dataset curation, LLM pretraining, sparsity, and various domains (coding agents, reasoning agents, generative language, image, video). The role involves building the team, formulating research agendas, adapting algorithms to Cerebras hardware, training/tuning/evaluating models, and collaborating with internal and external partners.

Staff Inference ML Runtime Engineer at Cerebras Systems, focusing on optimizing and scaling their wafer-scale AI chip for high-throughput, low-latency generative AI inference. The role involves designing and implementing ML features, APIs, and distributed runtime solutions, working with state-of-the-art generative AI models and multimodal data.

Senior Runtime Engineer role at Cerebras, focusing on designing and developing high-performance distributed software for large-scale AI training and inference workloads on their wafer-scale architecture. The role involves optimizing compute and data pipelines, ensuring scalability, and collaborating with ML and compiler teams. Requires strong C++ and distributed systems experience, with familiarity in ML pipelines preferred.

Kernel Engineer role focused on developing and optimizing high-performance software for Cerebras' AI chip, specifically implementing and scaling deep learning operations and building parallel algorithms for training and inference. The role involves low-level programming, performance tuning, and interaction with hardware architects to maximize compute utilization and accelerate AI innovation.

Cerebras is seeking an LLM Inference Performance & Evals Engineer to optimize and validate state-of-the-art models on their wafer-scale AI hardware. The role involves prototyping architectural tweaks, building performance-evaluation pipelines, and collaborating with hardware and software teams to accelerate new model ideas and improve inference speeds.

Cerebras is seeking a Full Stack LLM Engineer to join their Inference Core Model Bringup team. This role involves bringing up state-of-the-art open-source and proprietary models on Cerebras CSX systems, working across the entire software stack from model translation and compiler optimizations to runtime integration and performance tuning. The engineer will debug performance and correctness issues and propose improvements to tools and automation. Experience with deep learning frameworks, model internals, C/C++, and compiler development (LLVM/MLIR) is required.

Senior Performance Engineer focused on benchmarking Cerebras' AI inference performance against competitors and analyzing pricing models. Requires deep expertise in open-source inference stacks, GPU optimization, and LLM inference economics.

Engineering Manager for Inference ML Runtime at Cerebras, leading a team to design and scale systems for executing state-of-the-art AI models on Cerebras hardware. The role focuses on ML, distributed systems, and high-performance runtime engineering, with a goal of delivering the fastest Generative AI inference solution.

ML Performance Benchmarking Engineer role focused on optimizing AI inference performance on Cerebras' wafer-scale architecture. Responsibilities include building observability and benchmarking infrastructure, performance analysis, and integrating new inference features. Requires strong Python/C++ and infrastructure scaling experience, with a focus on complex, large-scale systems.

New Grad ML Stack Optimization Engineer role at Cerebras, focusing on optimizing compiler technologies for AI chips using LLVM and MLIR frameworks to enhance performance and efficiency of AI applications on their wafer-scale architecture.

ML Systems Performance Engineer at Cerebras, focusing on optimizing end-to-end model inference speed and throughput on their wafer-scale AI chip. Responsibilities include kernel optimization, system performance analysis, and developing performance modeling and diagnostic tools.

Product Manager for AI Models at Cerebras, focusing on defining and launching the strategic model portfolio for their wafer-scale AI inference platform. Responsibilities include roadmap ownership, partnerships with AI labs and open-source communities, defining quality standards, leading go-to-market strategies, and making technical decisions on performance optimizations. The role requires strong product management experience, technical knowledge of AI models and inference, and cross-functional leadership.

The Performance & Reliability Engineer will characterize and optimize the performance and reliability of advanced ML hardware/software systems, focusing on reducing power and thermal fluctuations. This role involves analyzing ML workloads, software kernels, and hardware architecture, developing software solutions for reliability and performance, and influencing next-generation AI architecture design.

Staff Python/PyTorch Developer for Frontend Inference Compiler at Cerebras, focusing on optimizing generative AI models for their wafer-scale AI chip. Responsibilities include developing compiler infrastructure, analyzing new models, and improving inference performance.

Product Manager for Strategic Verticals at Cerebras, focusing on embedding with strategic customers to translate their ambitions into AI solutions using Cerebras' wafer-scale architecture. The role involves owning customer success, designing PoCs, navigating complexities, and influencing the product roadmap by distilling customer insights.

Software Engineer to implement and optimize high-performance, low-latency inference services on Cerebras' wafer-scale AI chip, focusing on Kubernetes deployment, resource management, and reliability. This role involves collaborating with ML engineers, debugging complex issues, and ensuring the scalability and fault tolerance of AI inference workloads.

This role focuses on developing and maintaining cloud-based deployment workflows for AI inference software, utilizing containerization and orchestration technologies like Docker and Kubernetes. The responsibilities include ensuring system resiliency, high availability, and optimizing performance for low-latency inference tasks. The role also involves debugging, monitoring, and documenting inference services, with a strong emphasis on infrastructure-as-code and CI/CD practices.

Cerebras is seeking a Compiler Engineer to work on their Tungsten language compiler, which is purpose-built for their wafer-scale AI hardware. The role involves designing and implementing compiler passes, co-designing language constructs, and developing code generation strategies for AI and scientific workloads. The engineer will collaborate with ASIC, kernel, and AI teams, and contribute to the broader toolchain including runtime and debuggers. Experience with novel architectures and ML compiler frameworks is valuable.

The QA Lead will be responsible for ensuring the quality of Cerebras' software across all supported ML workloads and workflows, focusing on feature testing, ML training accuracy and performance, and pre-deployment validation. This role involves driving quality, implementing testing methodologies, automating workflows, and debugging issues within a large-scale enterprise environment.

ML Software Tool Development Engineer at Cerebras, focusing on building debugging, validation, and observability platforms for AI systems, including compilers, runtimes, and hardware interfaces. The role involves developing automated systems for anomaly detection, root-cause analysis, and visualization tools to support large-scale ML applications and inference.

Senior ML Software Engineer focused on integrating and validating the software stack for the Cerebras AI platform, ensuring reliable and efficient execution of large-scale ML workloads. This role involves debugging complex distributed systems, improving automation, and enhancing the reliability of AI infrastructure, working closely with runtime, compiler, kernel, and hardware teams.

Principal Engineer, AI Inference Reliability at Cerebras, focusing on ensuring the reliability, performance, and security of their large-scale AI inference services built on wafer-scale architecture. The role involves defining reliability strategy, implementing mechanisms for fault tolerance, leading incident management, and collaborating across engineering teams to meet world-class reliability standards.

Cerebras is seeking a Site Reliability Engineer to support their high-performance AI inference services powered by the Wafer-Scale Engine. The role involves operational execution, developing self-service CD pipelines, building automation tools, and enhancing observability for large-scale AI infrastructure. The position requires production Kubernetes experience and proficiency in Python or Go.

Staff Site Reliability Engineer focused on building and scaling high-performance SRE functions for Cerebras' AI inference services, powered by their Wafer-Scale Engine. The role involves leading engineering efforts to implement self-service delivery pipelines, shared observability tooling, and GitOps-driven CD for model releases and cluster management. The goal is to enable core teams, product managers, and external customers to operate in a fully self-service model with strong reliability guarantees, while also mentoring early-career SREs. The role emphasizes turning complexity into reliability at scale for frontier AI inference.

Principal Engineer for Cerebras' Inference Cloud Platform, focusing on availability, latency, reliability, and multi-region scale for their AI chip-based inference solution. This senior IC role involves defining long-term architecture, driving execution on critical paths, and contributing production code for large-scale distributed systems.

The role focuses on optimizing the performance of Cerebras' Runtime software driver, which runs on x86 machines and supports their AI accelerator chip. Responsibilities include CPU and memory subsystem optimizations, developing efficient data movement algorithms, utilizing advanced CPU features, performance profiling, and influencing future hardware/software designs. The role requires strong C/C++ skills and experience in performance engineering and system-level tuning.

Staff Software Engineer role focused on building and operating the Inference Cloud Platform, responsible for availability, latency, reliability, and global scale of AI inference workloads. Requires deep expertise in distributed systems, high-QPS optimization, and experience with ML inference infrastructure.

The AI Infrastructure Operations Engineer will manage and operate Cerebras' advanced AI compute clusters, ensuring their health, performance, and availability. This role focuses on maximizing compute capacity, deploying container-based services, and providing 24/7 monitoring and support for large-scale machine learning infrastructure.

Software Development Engineer in Test (Cloud) for Cerebras, focusing on quality ownership and building scalable test infrastructure for their AI Inference Cloud platform, which utilizes their large-scale AI chip for training and inference.

This role focuses on post-silicon validation, testing, and debugging of Cerebras' AI chips, specifically their Wafer Scale Engines. Responsibilities include characterizing high-speed interfaces, supporting manufacturing operations, developing automated regression test scripts, and creating debug tools. The role requires a Master's degree and experience in hardware bring-up, debug, and high-speed interfaces.

Cerebras Systems is seeking a Physical Design Engineer to work on their AI chip. The role involves synthesis, place and route, timing closure, and verification of their wafer-scale design. The company builds the world's largest AI chip, providing significant compute power for AI training and inference.

This role focuses on building a prognostics and health monitoring (PHM) capability for Cerebras' AI hardware and systems. The engineer will develop frameworks to monitor, assess, and predict hardware health, transforming telemetry data into actionable insights for early detection of degradation and proactive failure prediction to ensure system availability and performance. It involves reliability engineering, data science, and system software integration.

The Director of Business Operations at Cerebras Systems will be responsible for building and scaling the business operations, analytics, and execution systems to support the company's growth in AI chip manufacturing and deployment. This role involves partnering with operational leaders, driving closed-loop operational change, developing KPI and reporting architecture, and ensuring data quality and system integration. While the company builds AI hardware and partners with AI companies like OpenAI, this specific role focuses on the business operations and analytics to support the scaling of these operations, rather than direct AI/ML model development or research.

This role is for an IT SRE Team Lead responsible for the reliability, availability, and performance of Cerebras' internal IT systems. The lead will build and manage a team focused on automation, observability, and incident response, treating infrastructure as code with measurable SLOs. While the company builds AI hardware and has AI customers, this specific role focuses on internal IT operations, though it mentions using AI coding tools for triage and bug fixes.

Cerebras is seeking a Cybersecurity GRC Manager to mature and scale governance, risk, and compliance programs. This role involves using AI tools to streamline GRC workflows, automate control testing, and manage security risk. The ideal candidate will have deep technical security acumen, GRC expertise, and experience with industry frameworks. This is a strategic, cross-functional role focused on ensuring the security, privacy, and regulatory compliance of the organization's posture.

This role is for a Senior Hardware Technical Program Manager at Cerebras, a company that builds large AI chips. The role focuses on managing the end-to-end hardware schedule for AI compute systems and data centers, including design, engineering improvements, software integration, and collaboration with various engineering and operational teams. The goal is to ensure the efficient creation and deployment of supercomputer systems for AI workloads.

The role is for a Security SWE on the AI cloud team, responsible for customer-facing inference, training, and admin consoles and API experiences. The focus is on building responsive, user-friendly frontend interfaces for developers using Cerebras' AI hardware.

Software engineer to join the Kernel Reliability team, focusing on improving the reliability of Cerebras' AI compute clusters and underlying inference, training, and internal production services. The role involves working closely with code, designing scalable solutions, and debugging complex issues.

The role focuses on developing software automation frameworks, tools, and applications to improve operational efficiency and streamline business processes within Cerebras Systems, which builds large AI chips and provides AI compute solutions. The engineer will collaborate with cross-functional teams to identify automation opportunities, build process automation systems, and create data-driven solutions. The position requires strong software engineering fundamentals, experience with automation tools, and Python development.

Cerebras is seeking a Full Stack Engineer to design, build, and maintain a manufacturing test software solution for their AI chip. This role involves developing user interfaces and data processing frameworks to improve manufacturing efficiency, quality, and scalability, collaborating with hardware design, engineering, operations, and data analytics teams.

This role leads brand expression and integrated marketing programs for Cerebras, a company that builds large AI chips. The VP will oversee creative vision, campaign strategy, and pipeline alignment to drive awareness and growth, working closely with product marketing, communications, and sales teams. Experience in AI or high-complexity technical markets is preferred.

Product Marketing Manager for AI Inference at Cerebras, focusing on positioning and promoting their wafer-scale AI chip's inference capabilities. The role involves creating technical content, building community and influencer programs, and driving organic growth by highlighting Cerebras' speed advantage in the AI market.

This role is for an Infrastructure Hardware Technical Program Manager responsible for the end-to-end delivery of server and network platform programs for Cerebras CS-3-based AI clusters. The role requires technical understanding of server and network systems, program management skills, and experience coordinating with vendors and internal teams. While the company builds AI hardware and infrastructure, the role itself is focused on the hardware program management rather than direct AI/ML model development or research.