微软Senior Software Engineer (Bing Fundamentals)
任职要求
Required Qualifications: • BS/MS in Computer Science, or equivalent experience• 5+ years industrial experiences on an Object-Oriented Language, such as C++ (preferred), C#, or Java Preferred Qualifications:• Experience on high-performance computing (e.g. cache/memory optimization, high-performance GPU programming, compiler-based optimization, fine-grained parallel library and runtime) or large-scale distributed systems (e.g., communication optimization, network architecture design, network programming) is a plus.• Capable of quantitatively implementing and qualifying a solution in a distributed environment with DevOps model• Experience on Search/Ads/Recommendation areas is a plus• Experience on performance analysis and optimization for both CPUs and GPUs, as well as good understanding on software-hardware codesign, is a plus. Microsoft is an equal opportunity emp…
工作职责
We are looking for motivated talents to build Core Search platform. You need to:• Design services at large scale, low latency, high reliability, low cost to maintain and operate.• Service performance profiling and scaling-up.• Advanced distributed service debugging. • Collaborating across multiple teams to design and deliver the solutions.
• Lead code reviews to ensure adherence to engineering standards, test coverage, and secure coding practices. Provide feedback and mentorship to peers, and apply tools and patterns that enhance reliability, diagnosability, and maintainability. • Develop scalable and secure design proposals, collaborating across teams to resolve dependencies and validate design hypotheses. Ensure solutions meet performance, compliance, and cost expectations. • Create and maintain test plans that validate functionality and security. Leverage automation and AI tools to improve test reliability and coverage, and ensure testability is embedded in design. • Apply secure design principles and engineering best practices to build resilient systems. Drive automation in deployment, ensure compliance with global regulations, and integrate security monitoring and incident response mechanisms. • Translate product requirements into actionable plans, estimate effort, and guide execution. Ensure safe deployment practices, flighting strategies, and rollback plans are in place to support efficient and secure releases. • Integrate telemetry and observability into systems to monitor performance and security. Act as a Designated Responsible Individual (DRI), lead incident response efforts, and continuously improve live site operations and support documentation. • Collaborate with stakeholders to understand user needs and incorporate feedback into product design. Ensure privacy and security requirements are met, and establish feedback loops to measure impact and value.
Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error — this is truly an extraordinary time and the era of AI has begun. Image recognition and speech recognition — GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. The GPU started out as the engine for simulating human creativity, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA’s GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and AI come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for Deep Learning, and NVIDIA is increasingly known as “the AI computing company.” Make the choice to join us today. Our team builds NVIDIA’s end-to-end autonomous driving application.We are seeking senior software engineers who are passionate about performance with interest in optimizing self-driving solutions that run on NVIDIA’s multi-computer and heterogenous HW architectures. What you’ll be doing: • Develop, maintain and optimize performance KPIs necessary to deliver NVIDIA’s L2/L3/L4 autonomous driving solutions • Devise acceleration strategies and patterns to improve software architecture and its efficiency on our computers with multiple heterogeneous hardware engines while meeting or exceeding product goals • Develop highly efficient product code in C++, making use of algorithmic parallelism offered by GPGPU programming (CUDA)/ARM NEON while following quality and safety standards such as defined by MISRA • Diagnose and fix performance issues reported on our target platform including on-road & simulation
NVIDIA data center systems, such as DGX and HGX, have become core to NVIDIA's rapidly growing enterprise and cloud provider businesses. These platforms bring together the full power of NVIDIA GPUs, NVIDIA NVLink, NVIDIA InfiniBand networking, NVIDIA Grace CPUs, and a fully optimized NVIDIA AI and HPC software stack. We are hiring Sr. Software Engineer who will help build simulators for our DGX Server platforms. Simulations play a significant role in building scalable systems at Speed of Light! You will work with world class engineering teams across HW and SW. What you’ll be doing: • Contribute to architect and develop simulation platform for next-gen NVIDIA DGX platforms. • Build, integrate and enhance simulator components with new HW features and write supporting technical documents. • Bring full SW stack up on DGX Simulator; work closely with hardware modeling, kernel & platform driver teams distributed globally. • Improve performance, fix bugs across user and kernel stack, and automate execution flow.
"1. Responsible for the research and development of data platfrom for xiaomi internet businesses. 2. Build the infrastructure and tools required for optimal extraction, transformation, and loading of data from a wide variety of data sources 3. Design and implement Data as a Service ( DaaS ) for analytics and data scientist team members that assist them in developing intelligent agile operation Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc."