NVIDIA

Solutions Engineer

• Compose, build, and productionize agentic AI solutions, tools, and applications for the NVIS delivery organization
• Develop LLM-based agents, skills, tool-calling workflows, orchestration logic, backend services, APIs, data pipelines, and automation features as part of NVIS Central
• Translate field, delivery, operations, and product needs into clear technical builds, agent workflows, and working software
• Develop agents that reason across project data, knowledge bases, operational systems, logs, reports, and delivery workflows
• Build workflows to identify risks, summarize project status, automate repetitive tasks, improve readiness visibility, and simplify handoffs
• Work with timely engineering, retrieval-augmented generation, context management, agent memory, function calling, evaluations, and guardrails to build reliable AI systems
• Integrate LLMs and agents with internal systems, project data sources, knowledge repositories, reporting tools, and operational workflows
• Collaborate with software developers, architects, product managers, DevOps/SRE, and NVIS field teams to implement reliable and scalable solutions
• Contribute to engineering guidelines covering code quality, testing, CI/CD, observability, documentation, security, and production support

Software Solution Architect

Engineering Manager

• Lead, grow, and mentor a cross-site team of power and infrastructure engineers across Pune and Bangalore
• Set individual and team roadmaps, development plans, and ownership across mandates
• Drive power profiling across pre-silicon engagement, power target qualification, regression monitoring, battery-life validation, and external software release qualification
• Run nightly development-branch and release-branch power and performance-per-watt monitoring across discrete GPU and data-center platforms
• Own a temperature-controlled, instrumented measurement lab across two sites, including DUT fleet management, board procurement, logistics, instrumentation, rework, DAQ and power-path integrity, rack build-out, and capacity planning
• Shape measurement strategy from pre-silicon through production and establish measurement confidence levels
• Drive instrumented boards, rail maps, on-board sensors, and DAQ adapter design
• Specify, track, and escalate measurement prerequisites and power targets
• Quantify and attribute power gaps and drive debugging and optimization with software and hardware power owners
• Deliver power verdicts supporting program go/no-go decisions and qualify external software drops
• Lead cross-company power engagement with OEMs and software power owners
• Co-engineer battery-life targets, conduct joint root-cause analysis, and maintain a shared bug-and-analysis stream
• Own the power profiling framework, power analytics dashboard, and automated power regression farm as production-grade shared assets
• Deliver energy-regulation compliance evidence for idle, standby, and off-mode power release gates

System Software Manager – Power Profiling, Infrastructure

Production Engineer

• Build and operate automation for large-scale GPU clusters across NVIDIA Cloud Partners and on-prem environments
• Develop tools and services for provisioning, validation, upgrades, monitoring, repair, and cluster lifecycle operations
• Improve Day 0 / Day 1 / Day 2 workflows for cluster bringup, handoff, and production operations
• Reduce manual production touches through APIs, GitOps, automation, and agent-assisted workflows
• Participate in on-call, incident response, debugging, and durable follow-up work
• Partner with platform, storage, networking, security, and workload teams to make infrastructure production-ready

Senior Software Engineer, DGX Cloud Production Engineering

Network Engineer

• Set the architecture for a unified platform spanning topology, configuration, telemetry, active measurement, change intelligence, and self-service experiences
• Build an authoritative network model connecting physical topology, logical overlays, configurations, and service dependencies
• Catch physical and logical configuration errors during cluster bring-up
• Define telemetry and measurement strategy using reachability tests, streaming signals, traffic and capacity data, and change and maintenance events
• Deliver health, path, blast-radius, and self-service exploration products
• Establish interfaces, data contracts, quality standards, and operating mechanisms
• Drive cross-functional roadmaps and make tradeoffs among speed, cost, reliability, and maintainability
• Mentor engineers through critical designs

Senior Software Engineer, Network Visibility Platform

• Assist network and AI cluster support teams in reproducing, resolving, and root-causing sophisticated customer issues
• Work with R&D teams to develop bug fixes, workarounds, and solutions for critical customers using NVIDIA network technologies
• Become an authority in NVIDIA network technologies used in AI clusters, including InfiniBand, NVLink, and Spectrum-X
• Analyze network performance metrics and make tuning recommendations for high-performance, lossless networks
• Develop support and analysis tools to analyze and root-cause field issues
• Use AI tools daily for software development, log and trace analysis, and source code debugging
• Occasionally work on weekends or holidays to support customers
• Interact with NVIDIA teams and internal and external customers

Senior Solution Engineer, Networking

Security Engineer

• Own the end-to-end security story for specific subcomponents, from threat model through hardening and verification
• Design, prototype, and drive production adoption of mitigation and hardening technologies, including hardware CFI, Pointer Masking, and Memory Tagging
• Build and evangelize tools, practices, and processes that improve product robustness across the company
• Perform offensive security research across GPU and platform firmware, microcode, kernel drivers, and embedded software
• Identify vulnerabilities, build proof-of-concept exploits, and partner with development teams to remediate them
• Perform threat analysis and security reviews of software and hardware designs

Senior Offensive Security Researcher, GPU System Software

Full-stack Engineer

• Develop programs in PTX, CUDA, C/C++, or low-level GPU languages to validate compiler and evaluate generated code across key features and architectures
• Build software components, libraries, and technical solutions that advance compiler development and open up more innovative ways to explore and exercise compiler behavior
• Collaborate with compiler and hardware engineers on challenging architectural and compiler interactions, helping uncover deeper insights into how advanced GPU software features behave across releases
• Contribute to expansive engineering projects that improve the robustness, scalability, and future-readiness of the CUDA software stack for new GPU platforms

Software Engineering Intern, Compiler Verification

General

• Write highly tuned compute kernels to perform core deep learning operations, including matrix multiplies, MoE, and Attention
• Follow software engineering best practices, including regression testing and CI/CD flows
• Collaborate with the Compiler team on generating optimal assembly code
• Collaborate with deep learning training and inference performance teams to identify layers requiring optimization
• Collaborate with hardware and architecture teams on the programming model for new deep learning hardware features
• Develop optimized code to accelerate linear algebra and deep learning operations on NVIDIA GPUs
• Tune parallel algorithms and analyze their performance

Performance Software Intern, Deep Learning Libraries

Artificial Intelligence

• Develop high performance operators on NVIDIA GPUs for cuBLAS, TensorRT, cuDNN, cuSparse and cuTensor libraries
• Analyze the performance of various GPU kernels on existing/new architecture
• Identify bottlenecks and propose creative solutions to improve GPU kernel performance
• Design and develop software for kernel authoring and shipping
• Adopt cutting-edge AI technologies in GPU kernel or similar development workflow
• Explore computer architectures for deep learning and work at the intersection of hardware and software
• Continuously explore and optimize the performance of operators and fusion operators in deep learning networks
• Design and develop scalable modular infrastructure for optimized operators used in training and inference

AI Computing Software Intern, GPU Kernel Libraries

Software Engineer

• Build and enhance high-performance LLM inference pipelines
• Analyze and optimize model execution, scalability, and memory usage
• Collaborate across framework and research teams to deliver efficient multi-GPU model serving
• Improve TensorRT compiler graph transformations and code generation for NVIDIA GPUs
• Develop compiler optimization passes
• Refine operator fusion and memory allocation
• Collaborate with CUDA and hardware architecture teams
• Design and tune GPU compute kernels and DSL implementations for GEMM, MoE, Attention, and Convolution
• Profile, analyze, and improve CUDA kernel performance to maximize GPU efficiency

AI Computing Software Development Intern

• Craft and develop robust inferencing software scalable across multiple platforms for functionality and performance
• Conduct performance analysis, optimization, and tuning
• Follow academic developments in artificial intelligence and large language models
• Provide feedback on architecture, hardware design, and development
• Collaborate across software, research, and product teams to guide machine learning inferencing direction
• Publish key results in scientific conferences

AI Computing Software Development Intern, LLM Inference

Designer

• Verify the design and implementation of the inference accelerator
• Verify ASIC design, architecture, reference models, and micro-architecture using advanced verification methodologies
• Understand unit design and implementation, define verification scope, develop verification infrastructure, and verify design correctness
• Collaborate with architects, designers, and pre- and post-silicon verification teams
• Implement and optimize automated verification flows to improve productivity and efficiency
• Stay updated on ASIC design verification trends and incorporate innovative techniques

Senior ASIC Design Verification Engineer – LPU

• Architect, build, implement, and debug data center power and performance-management software, emphasizing end-to-end power management at racks and data halls
• Develop real-time controllers, policies, and mechanisms that dynamically optimize performance, power, thermals, and energy efficiency under demanding data-center workloads
• Drive features across requirements, architecture, implementation, pre-silicon validation, silicon bring-up, productization, and production support
• Collaborate with GPU architects and hardware designers to define hardware-software interfaces and influence power-management capabilities of next-generation processors
• Analyze interactions among workloads, clocks, voltages, power limits, thermals, telemetry, and system-level policies
• Lead investigation and resolution of power, performance, stability, and reliability issues across firmware, drivers, hardware, and platform software
• Develop validation strategies and automation for functional correctness, transition latency, performance-per-watt, and robustness across operating conditions
• Build architecture, interface, and compose specifications for new power-management features
• Partner with geographically distributed architecture, ASIC, firmware, driver, validation, platform, and data-center systems teams

Senior System Software Engineer – Datacenter Power Management

Security Operations

• Collaborate with Hardware, Software, and System Architecture teams to define end-to-end solutions
• Define the firmware architecture for new products and features at the core of NVIDIA's SOC and GPU firmware and software
• Lead the development and implementation of strategic technologies, including functional safety, security, and next-generation system-on-chip and graphics processing unit capabilities
• Drive technical direction across multiple teams while ensuring scalable, maintainable, and high-quality firmware architecture

Senior System Firmware Engineer – SOC, GPU

Backend Engineer

• Scope and build tools, microservices, workflows, and distributed applications to accelerate data mining and AI training
• Design and implement solutions for streaming, resilience, logging, security, authentication, workflow orchestration, and data management
• Deploy AI models
• Design and develop Retrieval-Augmented Generation workflows enabling hybrid and agentic patterns
• Analyze and operationalize complex distributed systems for speed-of-light performance
• Develop data engineering solutions for AI developers in autonomous vehicle domains
• Process massive volumes of autonomous vehicle data for video data curation, behavioral search, and AI dataset management