Phd Intern Ai Ml Wireless L1/l2 - Spring jobs at Nvidia in India, Bengaluru

What you'll be doing:

As a member of Aerial RAN team working on AI Native stacks, you will be contributing to

• Develop and Optimize AI / ML modules for functional blocks specifically in wireless signal processing

• Perform literature survey to understand the prior art on AI/ML for RAN

• Analyze and identify the suitable ML architecture for the RAN functions of interest.

• Identify the right ML Architecture, complexity for each of the functional blocks

• Collaborate with multi-functional teams to optimize the OTA performance and compute complexity with DevTech and other business units within NVIDIA

• Benchmarking of OTA performance improvements with AI models and compute needs on different platforms

• Iteratively train, test & modify Model Arch for performance improvements

What we need to see:

• Full time PhD student doing research in the fields of AI and Wireless domains, and able to work as an Intern for at least 6 months or more starting from last week of January 2026

• Thorough understanding of the wireless Layer1/Layer2 functions and algorithm aspects

• Excellent grip on AI and ML concepts, techniques and abreast of latest developments in this field

• Deep understanding of Transformers, CNNs and other ML Architectures and their use cases

• Hands on experience in simulating signal processing algorithms in Matlab and Python.

• Programming skills in C/C++

• Experience in analyzing the problem, identifying the right model architectures. developing Models, Training and Optimization, preferably on signal processing domains

from the crowd:

• Knowledge of CPU, DSP or GPU architecture, as well as memory, I/O and networking interfaces.

• Experience with programming latency sensitive, real-time, multi-threaded applications on CPUs and one or more of GPUs or DSPs or Vector processors.

• Appetite to learn the details of how next generations of GPU will operate and build an outstanding Software-Radio 5G/6G stack that can fully demonstrate their power.

• Familiarity with CUDA programming and NVIDIA GPU Architectures

What you'll be doing:

• Develop AI performance tools for large scale AI systems providing real time insight into applications performance and system bottlenecks.

• Conduct in-depth hardware-software performance studies

• Define performance and efficiency evaluation methodologies

• Automate performance data analysis and visualization to convert profiling data into actionable optimizations

• Support deep learning software engineers and GPU architects in their performance analysis efforts

• Work with various teams at NVIDIA to incorporate and influence the latest technologies for GPU performance analysis

What we need to see:

• Minimum of 8+ years of experience insoftware infrastructure and tools

• BS or higher degree in computer science or similar (or equivalent experience)

• Adept programming skills in multiple languages including C++ and Python

• Solid foundation in operating systems and computer architecture

• Outstanding ability to understand users, prioritize among many contending requests, and build consensus

• Passion for “it just works” automation, eliminating repetitive tasks, and enabling team members

Ways to stand out from the crowd:

• Experience in working with the large scale AI cluster

• Experience with CUDA and GPU computing systems

• Hands-on experience with deep learning frameworks (TensorFlow, PyTorch, JAX/XLA etc.)

• Deep understanding of the software performance analysis and optimization process

What You Will Be Doing:

• Architect, lead, and scale globally distributed production systems supporting AI/ML, HPC, and critical engineering platforms across hybrid and multi-cloud environments.

• Design and lead implementation of automation frameworks that reduce manual tasks, promote resilience, and uphold standard methodologies for system health, change safety, and release velocity.

• Define and evolve platform-wide reliability metrics, capacity forecasting strategies, and uncertainty testing approaches for sophisticated distributed systems.

• Lead cross-organizational efforts to assess operational maturity, address systemic risks, and establish long-term reliability strategies in collaboration with engineering, infrastructure, and product teams.

• Pioneer initiatives that influence NVIDIA’s AI platform roadmap, participating in co-development efforts with internal partners and external vendors, and staying ahead of academic and industry advances.

• Publish technical insights (papers, patents, whitepapers) and drive innovation in production engineering and system design.

• Lead and mentor global teams in a technical capacity, participating in recruitment, design reviews, and developing standard methodologies in incident response, observability, and system architecture.

What We Need to See:

• 15+ years of experience in SRE, Production Engineering, or Cloud Infrastructure, with a strong track record of leading platform-scale efforts and high-impact programs.

• Deep expertise in Linux/Unix systems engineering and public/private cloud platforms (AWS, GCP, Azure, OCI).

• Expert-level programming in Python and one or more languages such as C++, Go or Rust.

• Demonstrated experience with Kubernetes at scale, CPU/GPU scheduling, microservice orchestration, and container lifecycle management in production.

• Hands-on expertise in observability frameworks (Prometheus, Grafana, ELK, Loki, etc.) and Infrastructure as Code (Terraform, CDK, Pulumi).

• Proficiency in Site Reliability Engineering concepts like error budgets, SLOs, distributed tracing, and architectural fault tolerance.

• Ability to influence multi-functional collaborators and drive technical decisions through effective written and verbal communication.

• Proven track record to complete long-term, forward-looking platform strategies.

• Degree in Computer Science or related field, or equivalent experience

Ways to Stand Out from the Crowd:

• Hands-on experience building platforms for large-scale AI training, inferencing, and data movement pipelines.

• Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and orchestration frameworks (e.g., Ray, Kubeflow).

• Expertise in hardware fleet observability, predictive failure analysis, and power/resource-aware scheduling.

• Experience leading operational readiness efforts and reliability engineering in GPU-heavy environments.

• Track record of driving cultural improvements in incident management, root cause analysis, and postmortem processes across large teams.

What You Will Be Doing:

• Develop and maintain large-scale systems supporting critical use cases for AI Infrastructure, driving reliability, operability, and scalability across global public and private clouds.

• Implement SRE fundamentals, including incident management, monitoring, and performance optimization, while designing automation tools to reduce manual processes and operational overhead.

• Build tools and frameworks to improve observability, define actionable reliability metrics, and enable fast issue resolution, driving continuous improvement in system performance.

• Establish frameworks for operational maturity, lead sustainable incident response protocols, and conduct blameless postmortems to improve team efficiency and system resilience.

• Work with engineering teams to deliver innovative solutions, mentor peers, uphold high standards for code and infrastructure, and contribute to hiring for a diverse, high-performing team.
  

What We Need to See:

• Degree in Computer Science or related field, or equivalent experience with 12+ years in Software Development, SRE, or Production Engineering.

• Proficiency in Python and at least one other language (C/C++, Go, Perl, Ruby).

• Expertise in systems engineering within Linux or Windows environments and cloud platforms (AWS, OCI, Azure, GCP).

• Strong understanding of SRE principles, including error budgets, SLOs, SLAs, and Infrastructure as Code tools (e.g., Terraform CDK).

• Hands-on experience with observability platforms (e.g., ELK, Prometheus, Loki) and CI/CD systems (e.g., GitLab).

• Strong communication skills with the ability to convey technical concepts effectively to diverse audiences.

• Commitment to fostering a culture of diversity, curiosity, and continuous improvement.
  

Ways to stand out from the crowd:

• Experience in AI training, inferencing, and data infrastructure services.

• Proficiency in deep learning frameworks like PyTorch, TensorFlow, JAX, and Ray.

• A strong background in hardware health monitoring and system reliability.

• Hands-on expertise in operating and scaling distributed systems with stringent SLAs, ensuring high availability and performance.

• Proven experience in incident, change, and problem management processes, fostering continuous improvement in sophisticated environments.

What you'll be doing:

• Understand, analyze, profile, and optimize deep learning workloads on state-of-the-art hardware and software platforms.

• Build tools to automate workload analysis, workload optimization, and other critical workflows.

• Collaborate with cross-functional teams to analyze and optimize cloud application performance on diverse GPU architectures.

• Identify bottlenecks and inefficiencies in application code and propose optimizations to enhance GPU utilization.

• Drive end-to-end platform optimization from a hardware level to the application and service levels

• Design and implement performance benchmarks and testing methodologies to evaluate application performance.

• Provide guidance and recommendations on optimizing cloud-native applications for speed, scalability, and resource efficiency.

• Share knowledge and best practices with domain expert teams as they transition applications to distributed environments.

What we need to see:

• Masters in CS, EE or CSEE or equivalent experience

• 5+ years of experience in application performance engineering

• Experience using large scale multi node GPU infrastructure on premise or in CSPs

• Background in deep learning model architectures and experience with Pytorch and large scale distributed training

• Experience with application profiling tools such as NVIDIA NSight, Intel VTune etc.

• Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture. Experience with NVIDIA's Infrastructure and software stacks.

• Proven experience analyzing, modeling and tuning DL application performance.

• Proficiency in Python and C/C++ for analyzing and optimizing application code

Ways to stand out from the crowd:

• Strong fundamentals in algorithms and GPU programming experience (CUDA or OpenCL)

• Understanding of NVIDIA's server and software ecosystem

• Hands-on experience in performance optimization and benchmarking on large-scale distributed systems

• Hands-on experience with NVIDIA GPUs, HPC storage, networking, and cloud computing.

• In-depth understanding storage systems, Linux file systems, RDMA networking