TPU Jobs in Bengaluru

Minimum qualifications: Bachelor s degree in Electrical Engineering, Computer Science, or equivalent practical experience. 20 years of experience with chip design flow, chip architecture, design methodologies, physical design, and verification processes. Experience in leading chip development projects. Experience in working with external ASIC vendors. Preferred qualifications: Master's degree or PhD in Engineering, or a related field. Experience with ASIC design methodologies for front quality checks (e.g., Lint, CDC/RDC, Synthesis, design for testing, ATPG/Memory BIST, UPF, and Low Power Optimization/Estimation). Knowledge of data centers and cloud markets, technological and business trends, requirements, and ecosystem partners. Ability to motivate and focus a large collaboration to reach challenging goals. Excellent communication and facilitation skills. About the job In this role, you ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You ll be part of a diverse team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems. As a Chip Leader, you will be responsible for overseeing the design and development of AI accelerators for our data center. You will be responsible for leading the chip design, from architecture requirements up to tape-out. Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible. Responsibilities Own the chip development and execution. Accountable for Quality, Schedule and Performance, Power, Area (PPA), being the primary point of contact for day-to-day execution of chip development, planning and tracking. Coordinate the work of different disciplines, such as design, verification, and test, to ensure the chip meets all specifications and requirements. Collaborate with the leadership team of each chip project: Technical Program Manager, Design Verification lead, Physical Design lead, DFT lead, and architecture team, to make execution decisions and drive the development process. Resolve technical issues that arise during the chip development process. Ensure chip quality by implementing best practices and implementing quality control measures. Be responsible for project development with the highest quality, manage issues as they arise through design and implementation. Work with Software and Platform teams for hardware-software co-development. Qualification : Bachelors degree in Electrical Engineering, Computer Science, or equivalent practical experience.

Software Engineer III - AI/ML, Platforms and Devices Company: Google Location: Bengaluru, Karnataka, India Minimum Qualifications: Bachelor s degree or equivalent practical experience. 2 years of experience in software development with one or more programming languages, or 1 year with an advanced degree. 2 years of experience in data structures or algorithms. 1 year of experience in one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). Preferred Qualifications: Master's degree or PhD in Computer Science or a related technical field. Experience developing accessible technologies. About the Job Google's software engineers work on cutting-edge technologies that transform how billions of users connect, explore, and interact with information. Our products must handle data at a massive scale, far beyond web search. We seek engineers who bring innovative ideas from various fields, including information retrieval, distributed computing, large-scale system design, networking, data storage, security, artificial intelligence (AI), natural language processing (NLP), UI design, and mobile development. As a Software Engineer, you will work on training and optimizing complex machine learning (ML) models for the Tensor Processing Unit (TPU). By enabling models across diverse applications like camera, speech, Translate, TTS (Text-to-Speech), and others on Edge TPU, you will gain valuable experience in efficient model architectures, optimization techniques, and on-device machine learning at Google. You will also be responsible for managing project priorities, deadlines, and deliverables. Google's mission is to organize the world s information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, software, and hardware to create radically helpful experiences for users. We design and develop new technologies and hardware to make user interactions faster, more seamless, and powerful. Whether advancing form factors, improving interaction methods, or innovating new ways to capture and sense the world around us, our Devices & Services team is helping make people's lives better through technology. Responsibilities Write product or system development code. Collaborate with peers and stakeholders through design and code reviews to ensure best practices (e.g., style guidelines, accuracy, testability, and efficiency). Contribute to documentation or educational content and adapt based on product updates and user feedback. Triage product or system issues, debug, track, and resolve by analyzing the source of issues and their impact on hardware, network, or service operations. Implement solutions in one or more specialized Machine Learning (ML) areas, utilize ML infrastructure, and contribute to model optimization and data processing. Qualification : Master's degree or PhD in Computer Science or a related technical field.

Minimum qualifications: Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience. 10 years of experience with cloud native architecture in a customer-facing or support role. 5 years of experience with cloud infrastructure. 5 years of experience in a technical role focused on AI infrastructure or related areas Experience building and operationalizing machine learning models. Experience with GPU programming (e.g., CUDA, OpenCL) and optimization techniques. Preferred qualifications: Experience with high-performance computing (HPC) environments and contributions to open-source projects related to AI or infrastructure. Experience training and fine-tuning large models (e.g., image, language, segmentation, recommendation, genomics) with accelerators. Experience with performance profiling tools (e.g., TensorFlow profiler, PyTorch profiler, Tensorboard). Experience designing/architecting large-scale infrastructure farms for specialist AI use cases. Experience with running MLPerf benchmarks, distributed training and optimizing performance versus costs. Excellent communication, presentation, and teamwork skills. About the job The Google Cloud Platform team helps customers transform and build what's next for their business all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers developers, small and large businesses, educational institutions and government agencies see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners. As a Customer Engineer for AI Infrastructure, you will be the technical expert and trusted advisor for our customers, helping them design, deploy, and optimize AI solutions using cutting-edge hardware and software. Your focus will be on GPUs, accelerators (including FPGAs and ASICs), and Google TPUs. You will work closely with Sales, Product Management, and Engineering to ensure our customers achieve maximum value from their AI investments. You will be responsible for scaling and helping accelerate GCP AI Infrastructure business growth. Google Cloud accelerates every organization s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems. Responsibilities Be a trusted advisor to customers, helping them understand and incorporate AI accelerators into their overall cloud strategy by recommending migration paths, integration strategies, and application architecture that incorporate Google Cloud AI optimized infrastructure. Demonstrate how Google Cloud is differentiated, highlighting the power of accelerators by working with customers on proof-of-concepts, demonstrating features, optimizing model performance, profiling, and bench-marking. Influence Google Cloud strategy at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements. Travel to customer sites and events as needed. Be responsible for business growth and workload acceleration on AI infrastructure products and solutions for GCP. Qualification : Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.

Engineer, Principal/Manager - Machine Learning, AI Location: Bangalore, Karnataka, India Company: Qualcomm India Private Limited General Summary Qualcomm is seeking an experienced and visionary Principal AI/ML Engineer to lead research, development, and optimization of AI inference systems. This role involves developing high-performance AI models, optimizing deployments across various hardware platforms, and contributing to research in model compression, quantization, and hardware-aware optimization. Education & Experience PhD with 6+ years, Master's with 7+ years, or Bachelor's with 8+ years in Engineering, CS, or related field. 20+ years of experience in AI/ML development; 5+ years in inference optimization and debugging. Key Responsibilities Model Optimization & Quantization Optimize models using quantization (INT8, INT4, mixed precision), pruning, and knowledge distillation. Implement PTQ and QAT techniques for deployment. Experience with TensorRT, ONNX Runtime, OpenVINO, TVM. AI Hardware Acceleration & Deployment Target platforms: Hexagon DSP, CUDA GPUs, TPUs, NPUs, FPGAs, Habana Gaudi, Apple Neural Engine. Use Python APIs: cuDNN, XLA, MLIR for hardware acceleration. Benchmark and debug performance across platforms. AI Research & Innovation Research on efficient AI inference: model compression, low-bit precision, sparse computing. Explore architectures like Sparse Transformers, Mixture of Experts, Flash Attention. Publish in ML conferences: NeurIPS, ICML, CVPR; contribute to open-source projects. Technical Expertise Optimization of LLMs, LMMs, LVMs for inference. Deep Learning frameworks: TensorFlow, PyTorch, JAX, ONNX. Expert in CUDA, cuPy, Numba, TensorRT, ONNX Runtime, OpenVINO. Skilled in Python for scalable AI development. Experience with ML runtime delegates: TFLite, ONNX, Qualcomm AI Stack. Debugging: Netron, TensorBoard, PyTorch Profiler, Nsight, perf, Py-Spy. Cloud inference: AWS Inferentia, Azure ML, GCP AI Platform, Habana Gaudi. Hardware-aware optimization: oneDNN, ROCm, MLIR, SparseML. Contributions to open-source and research publications are a strong plus. Leadership & Collaboration Lead a team of engineers in Python-based AI inference and optimization. Collaborate with researchers, software engineers, DevOps, and hardware vendors. Define debugging, deployment, and performance tuning best practices.