Senior Deep Learning Engineer - Ai Wireless Systems jobs at Nvidia in Vietnam, Thái Nguyên

What You’ll Be Doing:

• Lead and grow a high-impact engineering team focused on AI-enabled signal processing for the Radio Access Network (RAN).

• Guide the development of deep learning models for tasks such as channel estimation, beamforming, link adaptation, and CSI compression.

• Collaborate with global teams across architecture, research, and systems to drive proof-of-concepts and production-quality AI-RAN components.

• Oversee integration of AI models into full-stack simulations and/or testbeds using frameworks such as PyTorch, TensorFlow, and NVIDIA Sionna.

• Align project priorities with hardware-software co-design constraints and deployment scenarios on NVIDIA platforms.

• Mentor team members, ensure technical excellence, and contribute to strategic direction.

What We Need to See:

• MS or PhD in Electrical Engineering, Computer Engineering, or related field.

• 10+ overall years of experience in wireless communications, signal processing, or AI/ML, with at least 3+ years of technical leadership experience.

• Deep understanding of wireless PHY/MAC systems, including MIMO, OFDM, and adaptive filtering.

• Proven experience developing or deploying neural network architectures (e.g., CNNs, Transformers) in real-world AI or signal processing applications.

• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.

• Strong collaboration and communication skills across multi-disciplinary teams and geographies.

Ways to Stand Out from the Crowd:

• Experience with AI for 5G/6G systems, AI-for-RAN architecture, or telecom-grade deployments.

• Knowledge of channel estimation by AI, model compression, real-time inference, or GPU optimization

• Familiarity with RIS, massive MIMO, or THz communication challenges.

• Track record of research, publications, or open-source contributions in AI-for-wireless.

What You’ll Be Doing:

• Design and prototype deep learning models for wireless signal processing tasks such as channel estimation, beam alignment, link adaptation, and scheduling.

• Work with simulation tools and real-world datasets to build models that generalize across diverse wireless scenarios.

• Implement, train, and validate neural networks (e.g., CNNs, Transformers, GNNs) using PyTorch or TensorFlow.

• Collaborate with researchers and system engineers to integrate models into full-stack RAN.

• Optimize model performance for real-time inference and hardware acceleration.

• Contribute to model evaluation, benchmarking, and deployment readiness on GPU platforms.

What We Need to See:

• MS or PhD in Electrical Engineering, Computer Engineering, or a related field (or equivalent experience).

• 12+ years of experience in wireless communications, signal processing, or AI/ML.

• Deep understanding of communication systems (e.g., MIMO, OFDM, fading channels) and DSP fundamentals.

• Strong experience in training and deploying deep learning models for time-series or signal-based tasks.

• Proficiency in Python and experience with DL frameworks like PyTorch or TensorFlow.

• Familiarity with tools such as MATLAB, GNU Radio, or NVIDIA Sionna for wireless simulation.

Ways to Stand Out from the Crowd:

• Experience with AI for 5G/6G systems, AI-for-RAN architecture, or telecom-grade deployments.

• Knowledge of channel estimation by AI, model compression, real-time inference, or GPU optimization

• Exposure to CUDA, Triton, or real-time inference pipelines.

• Contributions to research publications or open-source wireless/AI projects.

What you'll be doing:

• Develop and fine-tune multi-modal AI models using NVIDIA’s TAO Toolkit and deep learning frameworks.

• Contribute to the design and implementation of vision-language models (VLMs) and universal segmentation systems.

• Conduct experiments and benchmarking to evaluate model accuracy, robustness, and scalability.

• Collaborate with cross-functional teams to integrate your research into production-level pipelines and NVIDIA SDKs.

• Participate in research discussions, code reviews, and technical documentation to share insights and improve methodologies.

What we need to see:

• Currently pursuing a degree in Computer Science, Computer Engineering, or a related field.

• Proven experience with machine learning, deep learning, or computer vision model development.

• Strong Python programming skills and proficiency with PyTorch or similar frameworks.

• Solid understanding of neural network architectures, transformers, and multi-modal learning techniques.

• Excellent problem-solving abilities, attention to detail, and a collaborative mindset.

• Familiarity with vision-language models, image segmentation, or large-scale pretraining is a strong plus.

What you'll be doing:

• You will develop generative models for protein backbone/structure generation, prediction, and molecular docking using NVIDIA technology.

• Collaborate with AI experts to implement and refine algorithms.

• Conduct research and experimentation to enhance model performance.

• Work closely with cross-functional teams to integrate your models into existing platforms.

• Participate in regular team meetings to discuss progress and determine next research steps.

What we need to see:

• Currently pursuing a degree in Computer Science, Computer Engineering, or a related field.

• Proven experience with machine learning, and generative AI model development.

• Strong programming skills in Python, its libraries, and deep learning frameworks like PyTorch.

• Outstanding problem-solving abilities and strong attention to detail.

• Ability to work collaboratively in a diverse and inclusive environment.

• Familiarity with protein engineering, biotechnology, and molecular biology is a strong plus.

What You’ll Be Doing:

• Build, lead and scale world-class engineering teams in Vietnam, collaborating with global counterparts across system software, data science, and AI platforms.

• Drive the design, architecture, and delivery of high-performance system software platforms that power NVIDIA’s AI products and services.

• Partner with global teams across Machine Learning, Inference Services, and Hardware/Software integration to ensure performance, reliability, and scalability.

• Oversee the development and optimization of AI delivery platforms in Vietnam, including NIMs, Blueprints, and other flagship NVIDIA services.

• Engage with open-source and enterprise data and workflow ecosystems (e.g., Temporal, Gitlab DevOps Platform, RAPIDS, NeMo Curator, Morpheus) to advance accelerated AI factory, data science and data engineering workloads.

• Champion continuous integration, continuous delivery, and engineering best practices across multi-site R&D Centers.

• Collaborate with product management and cross-functional stakeholders to ensure enterprise readiness and customer impact.

• Develop and deploy standard processes for large-scale, distributed system testing, encompassing stress, scale, failover, and resiliency testing.

• Ensure security and compliance testing aligns with industry standards for cloud and data center products.

• Mentor and develop talent within the organization, fostering a culture of quality and continuous improvement.

What We Need to See:

• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or related field.

• 15+ overall years of software engineering experience with 6+ years in senior leadership roles.

• Proven record of managing large, high-performing software teams and delivering complex AI/ML or data-driven products.

• Expertise in cloud, data, and accelerated computing technologies (e.g., Spark, Kubernetes, Dask, Python ecosystem, CUDA).

• Experience collaborating with open-source communities and enterprise partners.

• Strong leadership, communication, and cross-functional coordination skills.

• Strategic mindset with hands-on technical depth in AI, system software, or large-scale data platforms.

Ways to Stand Out from the Crowd:

• Experience building and scaling AI/ML Inferencing platforms from concept to production.

• Background in GPU programming, CUDA optimization, or system performance engineering.

• Deep understanding of microservices, distributed systems, and high-performance data architectures.

• Contributions to open-source projects or developer ecosystems.

• Knowledge of deep learning, RAG, embeddings, or modern text search frameworks.

You will apply your expertise to develop highly available services that make effective use of the thousands of GPU involved in this operation. Your services provide the best-in-class performance, accuracy and availability. We are looking for technical talent to design, build, operate and improve our capabilities to produce NIMs at scale, including the underlying infrastructure, pipelines, inference backends, Docker build, test harness, metrics, performance engineering, log ingestion, and more.

What you'll be doing:

• Design, build, and optimize containerized inference execution for various AI applications, ensuring efficiency and scalability. These applications may run in container orchestration platforms like Kubernetes to enable scalable and robust deployment.

• Develop and deploy automation applications and microservices (e.g., in Python, Go) supporting the NIM factory.

• Ensure the performance, scalability, and availability of NIMs and the automation infrastructure through comprehensive performance measurement, monitoring, and optimization.

• Implement and manage CI/CD pipelines for automated testing and deployment.

• Apply container and orchestration expertise (Docker, Kubernetes) to create and optimize the basic building blocks of NIMs and automation tooling.

• Collaborate, brainstorm, and improve the designs of inference solutions with a broad team of software engineers, researchers, SREs, and product management.

• Mentor and collaborate with team members and other teams to foster growth and development. Demonstrate a history of learning and enhancing both personal skills and those of colleagues.

What we need to see:

• A history of using advanced programming skills (e.g., Python, Go) to build distributed compute systems, backend services, microservices, and cloud technologies.

• Experience productionizing and deploying various types of AI models (e.g., foundation models, computer vision, speech recognition).

• Experience implementing robust CI/CD pipelines for automated testing and deployment.

• Effective experience working with multi-functional teams, principals, and architects across organizational boundaries.

• Mentorship and the ability to grow teams and team members.

• Deep technical expertise in distributed containerized applications using Docker, Kubernetes, Cloud Endpoints, Helm, and Prometheus.

• Passion for building scalable and performant microservice applications.

• Excellent interpersonal skills and the flexibility to lead multi-functional efforts.

• Proven experience debugging and analyzing the performance of distributed microservices or cloud systems.

• A degree in Computer Science, Computer Engineering, or a related field (BS or MS) or equivalent experience.

• 1+ years of demonstrated experience in developing performant microservices, cloud software, and/or tooling roles.

Ways to stand out from the crowd:

• Experience with multiple container engines, internals of the container image and runtime.

• Prior experience in building and deploying containers for Microservices, Cloud, and On-prem deployments.

• Background with large-scale full-stack development.

• Experience delivering event-driven applications using services such as Temporal, Kafka, Redis, or similar.

• Previous work in large-scale backend development.

• Develop efficient infrastructure and tools for automating complex software processes.

• Drive Performance Optimization: Implement advanced test harnesses, benchmarking frameworks, and analytical tools to rigorously characterize and optimize the performance and efficiency of our software and hardware platforms.

• Apply deep knowledge of operating systems, kernel internals, device drivers, memory management, storage, networking, and high-speed interconnects to build and troubleshoot highly performant systems.

• Work with engineering teams to understand needs, define requirements, and deliver efficient solutions.

• Set performance goals, monitor feedback, analyze data, and make continuous improvements for system reliability.

• Influence Technical Strategy: Contribute to defining technical strategies and roadmaps for our platform automation initiatives, ensuring alignment with company-wide goals and standard methodologies.

• Bachelor's or equivalent experience in Computer Science, Computer Engineering, or a related technical field, or Master's degree or equivalent experience in a similar field.

• 5+ years of industry experience in software development, focusing on infrastructure, distributed systems, automation, and/or performance engineering.

• Expertise in System-Level Programming: Proven ability to develop robust tools and automation using programming languages such as C++, Python, or Go.

• Deep Understanding of System Software: Experience with operating system internals, device drivers, memory management, and debugging performance issues in complex compute applications.

• Distributed Systems: Experience in designing, building, and operating large-scale distributed systems, with knowledge of networking protocols, cluster management, and high-performance interconnects.

• Automation and CI/CD Proficiency: Experience building and maintaining automated testing, benchmarking, and continuousintegration/continuousdeployment pipelines.

• Problem-Solving and Analytical Skills: Outstanding analytical, problem-solving, and debugging skills, with a track record of resolving complex technical challenges.

• Collaboration and Communication: Excellent interpersonal and communication skills, with the ability to articulate complex technical concepts to diverse audiences and collaborate effectively across teams.

• Experience optimizing performance for AI/Machine Learning workloads, especially inference applications, on diverse hardware platforms.

• Prior experience building or contributing to large-scale compute infrastructure solutions in cloud environments or on-premises data centers.

• Experience with containerization and orchestration technologies, such as Docker and Kubernetes.

• Familiarity with performance profiling tools and methodologies for hardware and software systems.

• Track record of driving significant efficiency gains or architectural improvements in large-scale systems.