--- title: 'AI Performance Engineer at Modular' canonical: 'https://feeny.ai/job/ai-performance-engineer-modular-europe-qzyyx748n8jb' type: 'job' last_seen: '2026-11-05' --- # AI Performance Engineer at Modular - **Company:** Modular - **Location:** Europe - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-10-08 - **Last confirmed live:** 2026-11-05 - **Apply:** https://jobs.gem.com/modular/am9icG9zdDq3flYE-LztfB10bEJWwgDi ## Job description About the role: We are looking for an AI Performance Engineer to join the ASIC Performance team. You will design, implement, and tune high-performance attention kernels—and a broader set of foundational AI kernels—for new accelerator architectures. Attention is evolving quickly across models and hardware: grouped-query and multi-head attention, prefill and decode, paged KV caches, sliding-window and global attention, and new sparse or fused variants each create different compute, memory, and data-movement tradeoffs. In this role, you will translate those algorithmic requirements into production-quality implementations that exploit the unique capabilities of GPUs, NPUs, ASICs, and other emerging accelerators. You will also contribute to the architecture and development of Modular’s Attention framework, building reusable abstractions that let model and kernel developers express attention variants cleanly while supporting hardware-specific specialization and excellent end-to-end performance. This is a senior individual-contributor role for an engineer who can independently own ambiguous performance problems, collaborate across organizational boundaries, and turn research ideas and hardware capabilities into robust product technology. Our goal is to deliver performant tokens to end cloud users with production-ready workloads, not just microbenchmarks. LOCATION: Candidates based in Edinburgh are welcome to apply. You can work in our office in Edinburgh, or remotely from home. Onboarding for new hires is conducted in-person at the appropriate office. What you will do: - Design and implement high-performance attention kernels for prefill and decode, including variants such as MHA, GQA, paged attention, sliding-window/global attention, and fused FlashAttention-style algorithms. - Build and optimize general AI kernels—including matrix multiplication, softmax, normalization, activation, embedding, reduction, and data-movement primitives—needed to bring up modern models on new accelerators. - Contribute to the design and implementation of the Attention framework, with reusable interfaces that balance portability, composability, tunability, and hardware-specific optimization. - Profile workloads, identify bottlenecks, establish performance models, and tune implementations against hardware limits and competitive baselines. - Develop benchmarks, correctness tests, regression tests, and automated performance analysis to ensure kernels remain reliable and fast across model shapes and hardware generations. - Partner with the rest of Modular teams to contribute to overall software stack improvements and share learnings. - Work directly with hardware partners when needed to understand new architectures, validate toolchains, diagnose low-level issues, and influence hardware/software interfaces. - Write design documents, participate in technical reviews, and share architecture and performance insights across the team. - Mentor teammates and raise the engineering bar for kernel quality, performance methodology, and maintainable low-level software. What you bring to the table: - 5+ years of relevant industry or research experience in high-performance computing, AI kernel development, accelerator programming, compiler engineering, or a closely related field. - Demonstrated experience writing and optimizing production-quality GPU or accelerator kernels, with a strong understanding of parallel algorithms, numerical behavior, memory access patterns, synchronization, and data movement. - Strong knowledge of modern attention algorithms and their implementation tradeoffs, including the distinct performance characteristics of prefill and decode. - Strong understanding of architectures beyond conventional GPUs—such as NPUs, DSPs, TPUs, or custom ASICs—and the ability to reason about unfamiliar compute units, memory hierarchies, and execution models. - Experience with at least one heterogeneous programming model or kernel ecosystem such as CUDA, Triton, SYCL, OpenCL, HIP, or a vendor accelerator SDK. - Strong performance-analysis skills and proficiency with relevant profiling, tracing, benchmarking, and debugging tools. - Ability to read architecture manuals, compiler output, and low-level generated code; form hypotheses from data; and systematically close performance gaps. - A collaborative, team-oriented approach; clear written and verbal communication; intellectual curiosity; and comfort owning ambiguous technical problems from investigation through productization. Helpful but not required - Knowledge of the Mojo programming language or experience authoring kernels and performance-sensitive libraries in Mojo. - Knowledge of MLIR, LLVM, and related AI compiler technologies, including dialect design, lowering pipelines, code generation, scheduling, or autotuning. - Experience implementing FlashAttention or other fused attention algorithms, custom attention operators, paged KV-cache kernels, or inference-specific attention optimizations. - Experience with modern kernel DSLs and libraries such as CuTe/CUTLASS, Triton, Pallas, or similar systems. - Familiarity with AI framework internals and operator integration in systems such as PyTorch, JAX, TensorFlow, vLLM, SGLang, or TensorRT-LLM. - Experience bringing up a model or kernel library on a new hardware platform, including working through compiler, runtime, driver, and hardware constraints. - Experience with performance modeling, autotuning, numerical validation, or benchmarking infrastructure. - Familiarity with LLM architectures such as GPT and Gemma, including grouped-query attention, mixture-of-experts, speculative decoding, and long-context inference. - An advanced degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field. What Modular brings to the table: - Amazing Team. We are a progressive and agile team with some of the industry’s best engineering and product leaders. - World-class Benefits. In order to attract the best, we need to offer the best. Your benefits package may include comprehensive healthcare coverage, retirement and savings programs, employee stock purchase opportunities, paid time off, wellbeing resources, family support programs, and learning and development opportunities. Please note that specific benefit packages may vary based on your location, you can read more about [benefits offered by Qualcomm here](https://www.qualcomm.com/company/careers/benefits). - Competitive Compensation. We offer very strong compensation packages, including RSU grants. We want people to be focused on their best work and believe in tailoring compensation plans to meet the needs of our workforce. - Team Building Events. We organize regular team onsites and local meetups in Los Altos, CA as well as different cities. Traveling 2-4 times a year is expected for all roles. Working at Modular will enable you to grow quickly as you work alongside incredibly motivated and talented people who have high standards, possess a growth mindset, and a purpose to truly change the world. The estimated base salary range for this role to be performed in Edinburgh is £99,600–£150,000 GBR. The salary for the successful applicant will depend on a variety of permissible, non-discriminatory job-related factors, which include but are not limited to education, training, work experience, business needs, or market demands. This range may be modified in the future. The total compensation for a candidate will also include annual target bonus, equity, and benefits, with equity making up a significant portion of your total compensation. For candidates who fall outside of the listed requirements, we nevertheless encourage you to apply as we may have upcoming openings that are lower/higher level than the ones advertised. ## About Modular ## Company Overview - **One-liner**: Modular builds a unified, high-performance AI inference platform that enables developers to run AI workloads efficiently across any hardware. - **Entity Type**: Private (Acquired by Qualcomm in July 2026) - **Headquarters**: Los Altos, California, United States - **Founded**: 2022 - **Founders**: Chris Lattner and Tim Davis ## Core Business - **Primary industry/industries**: AI Infrastructure, Deep Learning, Software Development - **Target customers**: AI/ML engineers, data scientists, and enterprises deploying large-scale AI models (B2B, Enterprise) - **Mission statement**: "Make AI’s compute layer unified, efficient, and accessible to all." ## Products & Services - **Modular Platform (MAX)**: A unified AI inference platform offering text, audio, and image inference. It provides state-of-the-art performance with shared or dedicated endpoints, deployment in Modular's cloud or the customer's VPC, and support for custom models. It includes a high-performance, hardware-agnostic serving framework that automatically optimizes kernels across accelerators. - **Mojo**: A high-performance systems language designed for writing composable GPU kernels, enabling maximum performance across different hardware. ## Market Standing - **Valuation/Market Cap**: Not publicly available (acquired by Qualcomm). Total funding raised was $380M. - **Key Metric**: Total Funding of $380M, with an annual revenue of $1M as of the latest data. - **Notable Investors/Partners**: General Catalyst (lead, Series B), Google Ventures (lead, Seed), US Innovative Technology Fund (lead, Series C). - **Growth Signals**: Headcount grew by 39.9% year-over-year to 157 employees. The company was acquired by Qualcomm in July 2026, signaling a major strategic validation and exit. It maintains a strong technical workforce (110 out of 157 employees are in technical roles). ## Competitive Advantages - **Hardware Agnosticism**: The platform runs seamlessly across NVIDIA, AMD, Trainium, TPU, Qualcomm, Intel, ARM, and Apple silicon, providing true portability and preventing vendor lock-in. - **Full-Stack Optimization**: Optimizes from low-level GPU kernels to API endpoints, delivering significant performance gains (e.g., 2x improvement over vLLM on diverse hardware) and up to 50% cost savings. - **Founding Team**: Led by Chris Lattner (creator of LLVM, Clang, and Swift) and Tim Davis, with decades of experience building AI infrastructure at Google and other big tech companies. ## Strategic Focus - **Unifying AI Compute**: The company’s core mission is to solve the fragmentation in AI infrastructure by creating a modular and composable platform. - **Open Source & Ecosystem**: Modular is open-sourcing the Mojo language and the MAX engine to drive adoption and build a community. - **Post-Acquisition Integration**: Following the acquisition by Qualcomm, the strategic focus will likely shift towards integrating its technology into Qualcomm’s hardware ecosystem and scaling its deployment to edge and mobile devices. ## Why Work Here - **Cutting-Edge Technology**: Employees work on building deep learning infrastructure from silicon to system, offering full-stack mastery of the AI software/hardware stack. - **Strong Compensation & Benefits**: Offers leading medical, dental, and vision insurance, strong compensation and equity packages, a 401k plan with up to 5% match, generous parental leave, unlimited paid time off, and a $1,500 work-from-home stipend. - **Culture**: Described as intellectually curious, humble, and collaborative. The company values work/life balance and offers flexible work hours and hybrid work options. - **High Employee Ratings**: On LinkedIn, the company has a 4.3/5.0 employer rating, with perfect 5.0 scores for Culture and Career, and 4.6 for Work-Life and Compensation. - **Onboarding & Process**: Onboarding occurs onsite at the Los Altos office. The interview process is straightforward, generally taking about 4 weeks, and includes a culture interview. ## Sources 1. [Modular Careers Page](https://www.modular.com/company/careers) 2. [Modular About Us](https://www.modular.com/company/about) 3. [Modular Website](https://www.modular.com/) 4. [Modular LinkedIn](https://www.linkedin.com/company/modular-ai) 5. [Modular Careers on Gem](https://jobs.gem.com/modular) ## Other roles at Modular - [Technical Program Manager, Platform Engineering](https://feeny.ai/job/technical-program-manager-platform-engineering-modular-san-francisco-hpb2655n8cs8) — San Francisco, CA - [Mojo Debugger Engineer](https://feeny.ai/job/mojo-debugger-engineer-modular-remote-gccbm5r3xjq7) - [Platform Engineer, Workplace Engineering](https://feeny.ai/job/platform-engineer-workplace-engineering-modular-los-altos-cyeavzd1vcjh) — Los Altos, CA - [AI Kernel Engineering Manager](https://feeny.ai/job/ai-kernel-engineering-manager-modular-united-states-6wh2bfyym5z2) — United States - [Technical Product Marketing Manager](https://feeny.ai/job/technical-product-marketing-manager-modular-united-states-dtedgp0kdtsg) — United States - [Mojo Back-End Compiler Engineer](https://feeny.ai/job/mojo-back-end-compiler-engineer-modular-remote-ncf0qchaevg5) - [AI Runtime Engineer](https://feeny.ai/job/ai-runtime-engineer-modular-edinburgh-4y9j51sdexm8) — Edinburgh, United Kingdom - [Senior Technical Community Manager](https://feeny.ai/job/senior-technical-community-manager-modular-united-states-canada-srs5wh13vqqg) — United States / Canada - [Inference Systems Engineer](https://feeny.ai/job/inference-systems-engineer-modular-united-states-4gaygxb1r6bk) — United States - [Engineering Manager, Hardware Bringup](https://feeny.ai/job/engineering-manager-hardware-bringup-modular-united-states-canada-8wfmtksxz83t) — United States / Canada