Cerebras
Semiconductors · Wafer-scale AI chip
- HQ
- Sunnyvale, US
- Founded
- 2016
- Website
- cerebras.net
Semiconductors · Wafer-scale AI chip
Cerebras currently has 38 active AI-related job listings. The majority of these roles, 79%, are focused on serving infrastructure. The top hiring function is Engineering, with 32 roles. The company is actively hiring in the United States and Canada. Frequent tech tags include model_serving and inference_infra. In the last 30 days, Cerebras posted 4 new AI roles, representing a 20% decrease compared to the previous 30-day period.
Currently tracking 36 active AI roles, up 46% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $170k–$250k (avg $206k).
Cerebras currently has 39 active AI-related roles in our index. The most common open titles are: Kernel Engineer (2), ML Systems Performance Engineer (2), LLM Inference Performance & Evals Engineer, AI Infrastructure Operations Engineer, AI Models, Product Manager. Most positions are in Engineering and Research.
Cerebras's active AI hiring is concentrated in: serving infrastructure (85%), post-training (8%), pre-training (5%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Cerebras is hiring AI talent in: United States (23 roles), Canada (20 roles), India (6 roles), United Arab Emirates (3 roles).
Job postings at Cerebras most frequently reference: model serving, inference infra, fine tuning, llm observability, frontier research.
In the past 30 days, Cerebras has posted 4 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Advanced Technology: AI/ML Research Scientist 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. | Pretrain | 10 |
| Lead Full Stack Machine Learning Engineer This role focuses on bringing up and optimizing open-source AI models and frameworks on Cerebras' wafer-scale hardware. It involves working across the full software stack, from model translation and compiler optimizations to runtime integration and performance tuning, with a strong emphasis on debugging and improving the bring-up process for future models. | ServePost-train | 9 |
| ML Research Engineer (Inference) 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. | Serve | 9 |
| Advanced Technology: R&D Engineer - AI/ML, HPC 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. | Serve | 9 |
| Applied Machine Learning Research Scientist 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. | Post-trainData | 9 |
| Kernel Engineer 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. | ServePost-train | 9 |
| Senior ML Systems Engineer 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. | Post-trainServe | 9 |
| Applied AI/ML Scientist 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. | Post-trainAgent | 9 |
| Principal ML Investigator 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. | PretrainPost-train | 9 |
| Staff Inference ML Runtime Engineer 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. | Serve | 9 |
| Senior Runtime Engineer 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. | ServeAgent | 9 |
| Kernel Engineer 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. | ServePretrain | 9 |
| LLM Inference Performance & Evals Engineer 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. | ServeEval Gate | 9 |
| Full Stack LLM Engineer 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. | Serve | 9 |
| ML Systems Performance Engineer ML Systems Performance Engineer role focused on optimizing inference speed and throughput on Cerebras' custom wafer-scale AI chip. Responsibilities include building performance models, optimizing kernel microcode and compiler algorithms, debugging runtime performance, and developing performance visualization tools. Requires strong background in computer architecture, low-level deep learning math, and experience with performance profiling and optimization on CPU/GPU simulators. | Serve | 8 |
| Senior Performance Engineer, Inference 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. | Serve | 8 |
| Engineering Manager, Inference ML Runtime 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. | Serve | 8 |
| ML Performance Benchmarking Engineer 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. | Serve | 8 |
| New Grad - ML Stack Optimization Engineer 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. | Serve | 8 |
| Staff Kernel Optimzation Engineer Staff Kernel Optimization Engineer role focused on developing and optimizing high-performance software for Cerebras' custom wafer-scale AI chip, specifically for deep learning operations and inference. This involves implementing and debugging low-level kernels, mapping algorithms to hardware, and studying emerging AI trends to evolve kernel library architecture. The role contributes to accelerating AI innovation and delivering industry-leading training and inference speeds. | ServePretrain | 8 |
| ML Systems Performance Engineer 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. | Serve | 8 |
| AI Models, Product Manager 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. | ShipServe | 8 |
| Performance & Reliability Engineer 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. | Serve | 8 |
| Staff Python / PyTorch Developer — Frontend Inference Compiler – Dubai 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. | Serve | 8 |
| Product Manager, Strategic Verticals 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. | ShipServe | 8 |
| Software Engineer, Inference Platform Software Engineer for Cerebras' Inference Platform team, focusing on the orchestration layer for inference on datacenter clusters. Responsibilities include shaping platform direction, ensuring reliability and performance of active-active systems, writing production code, leading production issues, and partnering with ML/Product/Infra teams. Requires 3+ years of experience in distributed systems, Kubernetes, and building highly available, latency-sensitive systems. Experience with ML inference infrastructure is a plus. | ServeAgent | 7 |
| Staff Software Engineer, Inference Platform Staff Software Engineer for Cerebras' Inference Platform team, focusing on the orchestration layer for datacenter clusters. Responsibilities include platform direction, reliability, performance, execution on critical paths, production leadership, and technical influence. Requires 8+ years of experience in distributed systems, Kubernetes, and backend languages, with a plus for ML inference infrastructure experience. | ServeAgent | 7 |
| Member of Technical Staff (Software Engineer) 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. | Serve | 7 |
| Sr. Member of Technical Staff 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. | Serve | 7 |
| Advanced Technology: Compiler Engineer 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. | Serve | 7 |
| QA Lead (ML Integration and Quality) 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. | ServePost-train | 7 |
| ML Software Tool Development Engineer 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. | Serve | 7 |
| Senior ML Software Engineer - Integration & Quality 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. | Serve | 7 |
| Principal Engineer, AI Inference Reliability 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. | Serve | 7 |
| Site Reliability Engineer - Ops & Automation 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. | Serve | 7 |
| Staff Site Reliability Engineer – Automation and Platform 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. | Serve | 7 |
| Principal Engineer, Inference Cloud 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. | Serve | 7 |
| Performance Engineer 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. | Serve | 7 |
| Staff Software Engineer, Inference Cloud 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. | Serve | 7 |
| AI Infrastructure Operations Engineer 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. | Serve | 7 |
| Physical Design Engineer Cerebras Systems is seeking a Physical Design Engineer to work on the design and analysis of 3D integrated products, focusing on ASIC/SoC physical design, packaging, power, clock, and cooling analysis. The role involves R&D on novel concepts for 3D integration and requires extensive experience in physical design flows, verification methodologies, and optimization for power/performance/area. The company builds large AI chips and provides AI compute power for training and inference. | — | 5 |
| Sr. Staff/Staff Design Verification Engineer The Sr. Staff/Staff Design Verification Engineer at Cerebras will be responsible for ensuring the high-quality design of Cerebras' AI chips, which are designed for AI training and inference. This role involves developing verification strategies, creating reusable verification environments, implementing tests, managing regressions, and debugging complex issues across simulation, emulation, and silicon bring-up. The engineer will collaborate with cross-functional teams including architecture, RTL design, physical design, firmware, and validation to ensure first-time silicon success. The role requires deep knowledge of SystemVerilog testbench, UVM, and scripting languages like Python, with a strong emphasis on debugging and problem-solving skills. | — | 5 |
| ASIC Architect This role is for an ASIC Architect at Cerebras, a company that builds large AI chips. The architect will translate high-level architecture specifications into micro-architecture requirements, perform performance and power trade-offs, and identify hardware acceleration opportunities for AI workloads. While the company's product is AI-focused and used for AI training and inference, the role itself is in hardware architecture and performance modeling, not direct AI/ML model development. | — | 5 |
| Network Architect The Network Architect will design and architect front-end datacenter and interconnect fabrics for AI clusters, optimizing for high resource utilization, low latency, and high-throughput communication. This role involves building proof-of-concept implementations, automating deployment and configuration, and establishing SRE-grade telemetry and observability for network reliability. Responsibilities include leading network debugging in distributed systems, collaborating with vendors, and representing the company in industry forums. | — | 5 |
| Senior / Staff Technical Program Manager - Datacenter Capacity Delivery (E2E) This role is for a Senior/Staff Technical Program Manager responsible for the end-to-end delivery of data center capacity for AI workloads. The role involves managing the entire lifecycle from planning to operational readiness, orchestrating cross-functional teams, and ensuring alignment with AI infrastructure and hardware deployment schedules. While the company builds AI hardware and the role supports AI workloads, the core function is data center capacity delivery, not direct AI/ML model development or research. | — | 5 |
| Security & IT General Opportunities The IT & Security team at Cerebras, which builds a large AI chip, is looking for individuals to secure and scale enterprise IT, cloud, network, and infrastructure environments. This involves building automation, supporting security engineering, improving security practices, and developing processes for a rapidly growing organization supporting advanced AI workloads. | — | 5 |
| Software Development Engineer in Test (Cloud) 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. | Serve | 5 |
| Sr. Technical Staff 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. | — | 5 |
| Physical Design Engineer 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. | — | 5 |
| Prognostics & Health Monitoring Engineer 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. | Ship | 5 |