亚马逊Sr. Software QA Engineer Audio, Asia Tech Center

Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV and Amazon Echo. What will you help us create? The Role: As a Acoustic Software QA Engineer, you will work with consumer application testing expertise. You will join the team of hands-on, pro-active, self-motivated and seasoned SQA professionals. In this role, you will: - Create test plans and test cases - Efficiently execute test cases across all functional areas of our products especially have expertise in Audio. - Review product user interface for conformity to design guidelines - Find, isolate, document, regress, and track bugs through resolution - Interpret and report testing results, and be a vocal proponent for quality in every phase of the development process - Work with Software Development Engineers to understand the overall technical architecture and how each feature is implemented - Collaborate with US teams and domestic partners in acoustic lab setting up and management

The Role We are Tesla Energy Team. We are building highly distributed energy network to support company vision of accelerate world’s transition to a sustainable energy. The Energy Team is seeking hardworking and passionate software engineers as various levels. Responsibilities • Design and develop high quality, scalable and stable back-end services. • Develop back-end web services. • Follow Tesla’s high standards for security-best practices in all development. • Partner closely with security team for code analysis and design reviews. • Perform unit testing. • Process bug reports and release fixes. • Participate in code reviews. • Participate in agile processes. • Always think innovatively to solve customer problems.

THE ROLE:  AMD is looking for a senior software engineer to join our growing team. As a key contributor you will be part of a leading team to drive and enhance AMD’s abilities to deliver the highest quality, industry-leading technologies to market.      THE PERSON:  If you are passionate about AI/ML frameworks, model optimization, and efficient AI deployment on accelerator hardware, this is your opportunity. We are looking for an engineer with strong software development skills, hands-on experience in model compression and low-precision optimization, and the ability to use AI-assisted tools effectively while maintaining strong technical judgment and code quality.You will work with AI researchers, framework engineers, compiler/runtime teams, hardware experts, and customer-facing teams to build robust optimization software for real-world AI workloads across AMD platforms.  KEY RESPONSIBILITIES:  - Design, implement, and maintain model optimization features for AI workloads on AMD hardware platforms.- Develop quantization, low-precision, and compression capabilities for CNN, Transformer, LLM, and multimodal models.- Build production-quality Python tools, libraries, APIs, and framework components.- Support training and fine-tuning workflows for optimized models.- Analyze and debug accuracy, latency, memory usage, and deployment tradeoffs.- Collaborate across framework, compiler, runtime, hardware, and application teams.  PREFERRED EXPERIENCE:  - Experience in one or more areas: AI/ML frameworks, model optimization, quantization, training workflows, runtime integration, or accelerator-oriented deployment.- Hands-on experience with ML frameworks such as PyTorch, ONNX/ONNX Runtime, or similar.- Strong Python development and debugging skills.- Experience building production-quality tools, libraries, APIs, or framework components.- Solid understanding of CNN, LLM, or multimodal model architectures.- Ability to reason about accuracy, performance, latency, memory footprint, and deployment constraints.- Demonstrated ability to use AI-assisted tools effectively for software development, debugging, technical exploration, and productivity improvement.- Strong software engineering fundamentals and ability to work with geographically distributed teams.  ACADEMIC CREDENTIALS:  -BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related technical fields.  #LI-JW2

THE ROLE: We are looking for a dynamic, upbeat software engineer to join our growing team. Your work will focus on building robust, efficient software components that enable high-performance execution of large language models and multimodal models across multi-GPU systems. You’ll collaborate with internal GPU library teams and open-source maintainers to implement features that improve throughput, latency, and scalability. This role emphasizes full-stack development within AI inference systems, with a strong focus on model behavior and framework integration. THE PERSON: A motivated early-career software engineer with solid foundational skills in Python and/or C++ in Linux environments. The ideal candidate has hands-on experience or strong academic exposure to deep learning systems, understands LLM and multimodal model architectures, and is eager to write production-quality code that balances functionality, correctness, and performance. KEY RESPONSIBILITIES:  Deep Learning & LLM Framework Optimization: Experience with optimizing major DL/LLM frameworks (PyTorch, vLLM, SGLang) for AMD GPUs and contribute improvements upstream. Model-Aware Implementation: Build features that interact closely with LLMs and multimodal architectures (e.g., Llama, Qwen-VL, Wan), requiring understanding of attention mechanisms, cross-modal fusion, KV caching, and quantization. Performance-Conscious Coding: Write efficient, scalable code while considering memory usage, concurrency, and bottlenecks in multi-GPU environments. Profiling: Use profiling tools to evaluate the impact of your changes, identify regressions, and validate performance improvements as part of the development cycle. End-to-End Performance Engineering: Perform comprehensive profiling to identify bottlenecks and implement system, memory, and communication optimizations across multi-GPU and multi-node setups. Compiler & Pipeline Acceleration: Leverage compiler technologies and graph compilers to enhance the full deep learning and inference pipeline. Research & Advanced Techniques: Prototype and integrate emerging optimization methods such as speculative decoding and weight-only quantization into production systems. Cross-Team & Open-Source Collaboration: Collaborate with internal GPU library teams and open-source maintainers to align improvements and ensure seamless upstream integration. Software Engineering Excellence: Apply robust engineering practices to deliver maintainable, reliable, and production-quality performance optimizations. MANDATORY EXPERIENCE:  Software Engineering Skills: Familiarity in Python. Familiarity with C++ or async programming is a plus. Understanding of LLM or multimodal model concepts: Knowledge of transformer architectures, attention mechanisms, vision-language alignment, and inference pipelines (e.g., image + text input handling). Have theoretical grounding in Transformer/Attention/MoE/KV Cache, and quantization (FP8/FP4). Linux development environment: Comfortable using command-line tools, Git, and standard debugging/profiling utilities. End-to-End LLM Performance Engineering: Experience with profiling and diagnosing compute, memory, and communication bottlenecks across multi-GPU and multi-node environments. Software Engineering Excellence & Community Contribution is a plus: Solid Python/C++ coding skills and experience debugging and testing practices, proven ability to deliver maintainable performance-critical software, and a track record of open-source contributions with strong self-motivation. GPU Kernel Development & Optimization is a plus: Knowlege of high-performance GPU kernels tuning for AMD GPUs using HIP, CUDA, ASM, and tools like CK, CUTLASS, and Triton. Compiler & System-Level Optimization is a plus: Foundational knowledge of LLVM, ROCm, and compiler-driven techniques for improving kernel and system performance. Model Architectures & Optimization Expertise: Experience with multimodal models (e.g., Qwen-VL, Qwen-Image-Edit, Wan) or diffusion-based generative models. Familiarity with techniques like quantization, PagedAttention, continuous batching, or speculative decoding. Development Skills: Exposure to GPU computing (ROCm, CUDA) or performance profiling tools (e.g., PyTorch Profiler). Distributed Systems Experience: Experience with distributed inference for large-scale models (e.g., Tensor Parallel, Pipeline Parallel). ACADEMIC & PREFERRED QUALIFICATIONS:  Bachelor’s in Computer Science, Computer Engineering, Electrical Engineering, or a related field. #LI-EH1