--- title: 'Software Engineer, GPU Inference at Cerebras Systems' canonical: 'https://feeny.ai/job/software-engineer-gpu-inference-cerebras-systems-united-states-and-x676a96fke12' type: 'job' last_seen: '2026-09-08' --- # Software Engineer, GPU Inference at Cerebras Systems - **Company:** [Cerebras Systems](https://feeny.ai/companies/cerebras-systems) - **Location:** United States and, Canada - **Employment:** full-time - **Posted:** 2025-11-25 - **Last confirmed live:** 2026-09-08 - **Apply:** https://jobs.ashbyhq.com/cerebras/1c8cdbc4-bd74-46df-9645-bb910eaa7a37/application **Skills:** Python, C++, Multi-threaded programming, Performance optimization, System-level development, LLM serving frameworks, vLLM, SGLang, TensorRT-LLM, PyTorch, Agile development practices > Lead the design and implementation of high-throughput, low-latency inference runtime solutions for generative AI models on custom hardware. Drive technical initiatives, optimize performance, and guide a team of engineers to deliver scalable ML features and tools. ## Job description Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. ## ABOUT THE ROLE Cerebras is building a new generation of disaggregated AI inference systems https://www.cerebras.ai/press-release/amd-and-cerebras-announce-industry-leading-ultra-low-latency-and-high-throughput-ai-inference that combine GPU-accelerated prefill with ultra-fast decode on the Cerebras Wafer-Scale Engine. We are hiring a Software Engineer to productionize and optimize our GPU serving stack, working across our custom inference APIs, the vLLM serving runtime, the AMD ROCm software stack, and rack-scale AMD GPU infrastructure, to make this new serving path reliable, numerically correct, observable, and exceptionally performant. You will write production code, establish operational practices for a new accelerator fleet, and drive improvements in time to first token, throughput, tail latency, and capacity efficiency. This is a hands-on role requiring deep debugging and optimization across application, runtime, distributed systems, and hardware layers. ## RESPONSIBILITIES - Productionize the GPU inference stack. Design, build, deploy, and maintain the complete GPU prefill path, spanning API services, model-serving workers, vLLM, PyTorch, ROCm, GPU nodes, networking, and rack-scale infrastructure. - Own GPU operational readiness. Establish deployment, upgrade, rollback, health-checking, capacity-management, and failure-recovery practices for the AMD GPU fleet. Build automation that makes driver, firmware, runtime, model, and container compatibility explicit and reproducible. - Drive reliability in production. Define service-level indicators and objectives for GPU-backed inference. Improve fault isolation, graceful degradation, automated recovery, incident response, and post-incident remediation across the serving stack. - Improve inference performance. Profile and optimize time to first token, request throughput, tokens per second per GPU, tail latency, GPU utilization, memory efficiency, and rack-level capacity under representative production workloads. - Optimize model-serving behavior. Tune and improve scheduling, continuous batching, prefix caching, KV-cache management, tensor and expert parallelism, request admission, quantization, graph execution, and distributed communication. - Debug across system layers. Diagnose complex failures and performance regressions across application code, vLLM, PyTorch, ROCm/HIP, collective communication libraries, kernels, drivers, firmware, networking, and hardware. - Ensure numerical correctness. Build validation and regression infrastructure for model quality, numerical accuracy, precision changes, quantization, determinism, and compatibility across software and hardware releases. - Build performance and correctness infrastructure. Develop representative benchmarks, workload replay tools, profiling automation, release qualification, dashboards, and regression gates. Turn one-off investigations into repeatable engineering systems. ## MINIMUM QUALIFICATIONS - 5+ years of software engineering experience, including substantial individual-contributor ownership of complex production systems. - Experience building, operating, or optimizing production inference systems for large language models, multimodal models, or similarly demanding GPU workloads. - Strong programming ability in C++ and Python, including experience with multithreading, concurrency, memory management, and performance-sensitive software. - Hands-on experience with a high-performance model-serving framework such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or an equivalent internally developed system. - Strong understanding of GPU execution and performance, including asynchronous execution, memory movement, synchronization, kernel launches, communication overhead, and profiling methodology. - Experience debugging distributed systems across multiple layers rather than treating the serving framework or accelerator runtime as a black box. - Experience with Linux, containers, Kubernetes or comparable orchestration systems, observability, CI/CD, and operating latency-sensitive services in production. - Ability to design rigorous benchmarks, interpret noisy performance results, identify bottlenecks, and translate findings into production improvements. - Strong communication and technical leadership skills, with a demonstrated ability to drive ambiguous cross-functional projects to completion. - Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related discipline, or equivalent practical experience. ## PREFERRED QUALIFICATIONS - Experience with AMD Instinct accelerators and the ROCm ecosystem, including HIP, RCCL, rocprofiler, AMD SMI, AITER, hipBLASLt, Composable Kernel, or related libraries and tools. - Deep CUDA experience that demonstrates an ability to transfer GPU systems knowledge across accelerator platforms. - Experience modifying or contributing to vLLM, SGLang, PyTorch, Triton, TensorRT-LLM, or another open-source ML systems project. - Experience optimizing prefill-heavy or disaggregated prefill/decode inference architectures. - Understanding of KV-cache transfer, prefix caching, continuous batching, chunked prefill, request scheduling, and memory-aware admission control. - Experience with multi-GPU and multi-node inference, including tensor parallelism, pipeline parallelism, expert parallelism, RDMA, collective communication, and failure handling. - Experience optimizing Mixture-of-Experts or multimodal models. - Knowledge of GPU kernel optimization, operator fusion, graph capture, attention kernels, GEMM tuning, and communication/computation overlap. - Experience with reduced-precision inference and quantization formats such as BF16, FP8, FP4, INT8, or INT4, including validation of their numerical and model-quality effects. - Experience building numerical-comparison, determinism, model-validation, or performance-regression test systems. - Experience collaborating directly with accelerator vendors, framework maintainers, or open-source communities. ## Why Join Cerebras People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: 1. Build a breakthrough AI platform beyond the constraints of the GPU. 2. Publish and open source their cutting-edge AI research. 3. Work on one of the fastest AI supercomputers in the world. 4. Enjoy job stability with startup vitality. 5. Our simple, non-corporate work culture that respects individual beliefs. Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice. ## About Cerebras Systems ## Company Overview - **One-liner**: Cerebras builds wafer-scale AI chips (the largest semiconductors ever made) and supercomputers that deliver up to 15x faster inference than GPUs, offered both on-premises and via cloud APIs. - **Entity Type**: Public (NASDAQ: CBRS) — filed for IPO and now listed on the Nasdaq. - **Headquarters**: Sunnyvale, California, United States - **Founded**: 2015 - **Founders**: Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie, Jean-Philippe Fricker ## Core Business - **Primary industries**: Semiconductor manufacturing, AI computing hardware and software, cloud AI services. - **Target customers**: B2B – enterprises, government research labs, cloud service providers, and AI developers. - **Mission**: “Building impossible technologies so you can too” – accelerating AI through wafer-scale computing. ## Products & Services - **Wafer-Scale Engine (WSE-3)**: The world’s largest AI chip (215 mm²), 58x larger than a typical GPU, built for ultra‑fast AI training and inference. Manufactured by TSMC. - **CS-3 Supercomputer**: A single system powered by the WSE-3, delivering record-breaking AI performance for on-premises deployment. - **Cerebras AI Inference Cloud & Training Cloud**: Pay-as-you‑go APIs that provide drop‑in OpenAI‑compatible access to Cerebras’ hardware for serving, fine‑tuning, and pre‑training models. - **Condor Galaxy Network**: A series of supercomputers (e.g., CG‑1 with 4 exaFLOPs) built in partnership with G42 for high‑performance AI. ## Market Standing - **Valuation/Market Cap**: Not disclosed (recently public; market cap data not available in provided sources). - **Key Metric**: Annual revenue of $78.7 M (latest); total funding of $2.7 B across 15 rounds. - **Notable Investors/Partners**: G42, OpenAI (signed in 2026), Amazon Web Services (signed in 2026), Meta (Llama API), Perplexity, Mistral, Hugging Face, OpenRouter. - **Growth Signals**: - 34.3% YoY headcount growth (701 employees). - Rapid data center expansion across North America and Europe. - Won the HPCwire Readers’ & Editors’ Choice 2025 for Best AI Product/Technology. - Named to Forbes America’s Best Startup Employers 2026 and Fast Company Most Innovative Companies 2026 (AI). ## Competitive Advantages - **Wafer‑scale architecture**: Eliminates interconnect bottlenecks, reducing latency and delivering up to 15x faster inference than GPU clusters. - **Largest AI chip ever built**: 58x larger than any GPU, enabling massive on‑chip memory (SRAM) and compute density. - **Full‑stack offering**: Hardware + cloud APIs + on‑prem systems, giving customers deployment flexibility. - **Strong customer relationships**: Key contracts with OpenAI, AWS, G42, and Mayo Clinic (2024 Gordon Bell Prize work). ## Strategic Focus - **Scale inference capacity**: Building out data centers to become the world’s #1 provider of high‑speed AI inference. - **Deepen cloud partnerships**: Expanding pay‑as‑you‑go cloud access and enterprise deployments. - **Advance wafer‑scale technology**: Continuous R&D on WSE‑3 successors and software stack (compiler, ML workflows). - **Broaden model support**: Already serving Llama, Gemma, Qwen, Mistral, and more; integrating with Hugging Face and OpenRouter. ## Why Work Here - **Culture**: “Extraordinary people, breakthrough innovation, global impact” – the company highlights collaborative, low‑overhead teams with little bureaucracy. - **Work environment**: Hybrid/office with locations in Sunnyvale, San Diego, Toronto, and Bangalore. Inclusive and flexible policy. - **Perks**: Premium medical/dental/vision, life insurance, generous vacation, 401(k) and Group RRSP retirement plans, daily catered meals, healthy snacks, family‑friendly events (including CEO’s famous BBQ). - **Engineering focus**: Tackling fundamental challenges in chip design, system software, compiler technology, and ML workflows. Positions range from hardware (Design Verification, Manufacturing) to software (SRE, Compiler, Cloud). - **Philanthrophy**: Supports local communities and hosts students from around the world for Q&A. ## Sources 1. [cerebras.ai/company](https://www.cerebras.ai/company) 2. [cerebras.ai](https://www.cerebras.ai/) 3. [cerebras.ai/join-us](https://www.cerebras.ai/join-us) 4. [linkedin.com/company/cerebras-systems](https://www.linkedin.com/company/cerebras-systems) ## Other roles at Cerebras Systems - [Network Security Engineer](https://feeny.ai/job/network-security-engineer-cerebras-systems-sunnyvale-s1q0rza0gbsf) — Sunnyvale, CA - [Distributed Software Engineer](https://feeny.ai/job/distributed-software-engineer-cerebras-systems-sunnyvale-2y0byqqxk57d) — Sunnyvale, CA - [AI Inference Core - Infrastructure SW Engineer](https://feeny.ai/job/ai-inference-core-infrastructure-sw-engineer-cerebras-systems-sunnyvale-nbqee273jncn) — Sunnyvale, CA - [AI Inference Core - Senior SW Engineer for Platform & DevOps](https://feeny.ai/job/ai-inference-core-senior-sw-engineer-for-platform-devops-cerebras-systems-06p3hxeg5qb7) — Sunnyvale, CA - [AI Inference Core - SDET Technical Lead, Release Integration Testing](https://feeny.ai/job/ai-inference-core-sdet-technical-lead-release-integration-testing-cerebras-fvqxmj75wav1) — Sunnyvale, CA - [ML Systems Integration Engineer](https://feeny.ai/job/ml-systems-integration-engineer-cerebras-systems-sunnyvale-edvsd91tqp93) — Sunnyvale, CA - [Lead Systems Signal Integrity/Power Integrity Engineer](https://feeny.ai/job/lead-systems-signal-integrity-power-integrity-engineer-cerebras-systems-zqq3c0wfj2v1) — Sunnyvale, CA - [Power Engineering Architect](https://feeny.ai/job/power-engineering-architect-cerebras-systems-sunnyvale-xtzeptb5vdc0) — Sunnyvale, CA - [PCB Layout Engineering Lead](https://feeny.ai/job/pcb-layout-engineering-lead-cerebras-systems-united-states-em00ewnccr6z) — United States - [Network Security Engineer (Remote)](https://feeny.ai/job/network-security-engineer-remote-cerebras-systems-united-states-yfrjb08d4wh9) — United States