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Looking for a CPU Architect with expertise in HW/SW codesign of dense computational logic (e.g., vector, matrix)
The role includes but is not limited to:
Analysis of technical challenges in relevant use cases and determination of whether to solve them by a combination of new HW and new SW or by only one of these
Analyzes the bottlenecks of current CPUs on workloads that reflect CPU future usage.
Invents corresponding HW features and SW solutions to address above challenges. Evaluates feasibility tradeoffs, explores, and defines new approaches and novel architectures for CPU. Develops the end-to-end architecture of new instructions in cooperation with partners. Drives the inclusion of the feature in a CPU project working with micro-architects, designers and verification experts.
(Preferably) models CPU functionality, performance and power in simulators.
Provides experimental/proof of concept changes for proposing design alternatives meeting performance, power, area, and timing constraints.
Reviews and influences cross functional roadmaps.
Collaborates with SW and HW architects, design, verification, and validation engineers during the execution of the project. Finds mitigations for issues that arise during implementation of his/her features.

about the job
our company's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. our products need to handle information at massive scale, and extend well beyond web search. we're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data Storage, security, artificial intelligence, Natural Language Processing, UI design and mobile; the list goes on and is growing every day. as a software engineer, you will work on a specific project critical to our companys needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. we need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the Full-Stack as we continue to push technology forward.
in our company search, we're reimagining what it means to search for information - any way and anywhere. to do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. in joining the search team, you'll have an opportunity to make an impact on billions of people globally.
responsibilities
create new features and transition them into stable, production-ready systems for the windows our company app.
review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
partner closely with product managers, UX designers, and others to define technical requirements and drive the product roadmap forward.

As an ML Software Engineer with a focus on low-level and CUDA-based optimizations, you will play a key role in shaping the design, performance, and scalability of machine learning inference systems. Youll work on deeply technical challenges at the intersection of GPU acceleration, systems architecture, and ML deployment.
Your expertise in CUDA, C/C++, and performance tuning will be crucial in enhancing runtime efficiency across heterogeneous computing environments. Youll collaborate with designers, researchers, and backend engineers to build production-grade ML pipelines that are optimized for latency, throughput, and memory use, contributing directly to the infrastructure powering next-generation AI products.This role is ideal for an engineer with strong systems-level thinking, deep familiarity with GPU internals, and a passion for pushing the boundaries of performance and efficiency in machine learning infrastructure.

Responsibilities
Design and implement highly optimized GPU-accelerated ML inference systems using CUDA and low-level parallelism techniques
Optimize memory, compute, and data flow to meet real-time or high-throughput constraints
Improve the performance, reliability, and observability of our inference backend across diverse compute targets (CPU/GPU)
Collaborate with cross-functional teams (including researchers, developers, and designers) to deliver efficient and scalable inference solutions
Contribute to ComfyUI and internal infrastructure to improve the usability and performance of model execution flows
Investigate performance bottlenecks at all levels of the stack-from Python to kernel-level execution
Navigate and enhance a large, complex, production-grade codebase
Drive innovation in low-level system design to support future ML workloads