Cerebras

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.

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.

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.