NVIDIA

Technical Program Manager

• Collaborate with outstanding engineers and architects to build and deploy large scale GPU computing systems based on NVIDIA's reference supercomputing architectures
• Lead the integration of new AI clusters with datacenter facilities with demanding requirements on power, cooling, and instrumentation
• Coordinate design and fit-out of new datacenter builds, working with both internal engineering teams and external contractors
• Own and produce detailed documentation for the end-to-end process for datacenter fit-out and integration
• Communicate internally with engineering leadership to prioritize and address key issues essential to the success of our largest customers

Senior Datacenter Technical Program Manager – At-Scale AI Clusters

Computer Vision Engineer

• Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas.
• Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.
• Taking approaches from initial evaluation and experimentation all the way to shipping.
• Defining and collecting training datasets.
• Building training pipelines and real-time inference run-times (PyTorch, TensorFlow, TensorRT, Python, C++).

Senior Deep Learning and Computer Vision Engineer – Autonomous Vehicles

Account Manager

• responsible for all aspects of demand creation, co-selling, forecasting, sales leadership, training, and education to end users, OEMs, and partners
• Grow revenue and market share for NVIDIA Data Center, Edge, and Cloud products across the USAF and USSF
• Be the key point of contact and relationship owner for USAF and USSF customers, program offices, and mission partners
• Build key accounts into strategic partners and drive sustained, long-term growth within these organizations
• Collaborate closely with OEMs, software providers, system integrators, and research partners to craft and implement go-to-market plans
• Champion the use of NVIDIA’s accelerated computing platforms in support of USAF and USSF missions

Senior Account Manager – DoD

DevOps Engineer

• Manage a team of Software and Site Reliability engineers, including program development, task planning and code reviews.
• Define team strategy and roadmap, and drive adoption of scalable SDLC practices, test infrastructure, and modern practices.
• Drive technical projects and provide leadership in an innovative and fast-paced environment.
• Be responsible for the overall planning, tracking and success of technical projects.
• Work closely with project and product management teams to ensure best-in-class product development.
• Contribute technically to the technical projects for DGX Cloud Computing Services.
• Interact with key internal stakeholders to provide operational and financial clarity on technical spend.
• Lead efforts related to executive reporting, dashboards, and operational CTO metrics focusing on continuous improvement and evolution to maximize decision making and executive visibility.

Director, Site Reliability and Software Engineering

Solutions Engineer

• Guiding partners in their adoption of end-to-end Agentic AI solutions, using NVIDIA's compute, networking, and software stacks.
• Delivering demos, assisting with proof-of-concepts, or writing papers and developer blogs.
• Collaborating with executives and engineering to solve complex problems and help bring NVIDIA's premiere technologies to life in the cloud and in the datacenter.
• Solving problems that nobody else has solved yet and being an instrumental part of that.

Solutions Architect

Artificial Intelligence

• Develop and extend C++ and Python simulators that model system-level network and compute traffic for agentic LLM workloads in datacenter environments
• Characterize real-world LLM serving workloads and distill them into representative simulator inputs
• Run simulations at scale and apply statistical techniques to post-process and interpret results
• Identify performance bottlenecks and translate findings into concrete architectural recommendations
• Collaborate with hardware, software, and research teams to influence the design of future AI systems

Systems Performance Engineer – Agentic AI Workloads

Full-stack Engineer

• Lead initiatives to transform IT Compute platform architecture to build new service offerings across On-Prem & Cloud.
• Define and implement metrics to measure the efficiency of compute platforms & services and drive efficiency.
• Collect and review system data for capacity and planning purposes, analyze capacity data and develop plans for appropriate level enterprise-wide systems, and coordinate with management personnel in implementing changes.
• Develop and maintain tools for collecting, analyzing, and visualizing data for reporting, alerting, monitoring.
• Collaborate with NVIDIA leadership, senior engineers, program managers, and product managers to develop compelling IT products and services that meet customer needs.

Senior Staff Software Engineer

Mechanical Engineer

• Architecting the clock domain to satisfy functional, physical and testing design requirements.
• Engage with multiple teams and design the GPU or CPU clocks.
• Improve Power, Performance, and Area (PPA) of NVIDIA chips by evaluating trade-offs.
• Collaborate with Physical design and timing team to evaluate Clocking concerns.
• Deliver clock RTL information to GPU, CPU and SOC verification team, timing and DFT teams.
• Get involved in end-to-end cycle of ASIC execution starting from micro-arch.

ASIC Clocks Design Engineer – New College Grad

• Build innovative features for NVIDIA's GPU profiling tools from inception to execution
• Incorporate new hardware profiling capabilities into tools and workflows
• Work independently based on high-level requirements, filling in build details and making sound engineering decisions
• Collaborate with architects, performance engineers, and other software teams to understand requirements and deliver solutions
• Improve and maintain a large, evolving codebase with high standards for quality and reliability

Software Engineer, GPU Performance Tools

Architect

• Develop innovative architectures to extend the state of the art in deep learning performance and efficiency
• Analyze performance, cost and power trade-offs by developing analytical models, simulators and test suites
• Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications
• Evaluate PPA (performance, power, area) for hardware features and system level architectural trade-offs.
• Develop high level simulators in C++/Python
• Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW

Senior Deep Learning Performance Architect

LLM Engineer

• Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Perform root-cause analysis of failures in large distributed environments.
• Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster.
• Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.

Software Engineer, DGX Cloud AI Infrastructure

• Own and improve the end-to-end path for deploying Warp-based robotics and simulation components onto embedded platforms such as Jetson.
• Build and maintain reproducible deployment workflows including cross-compilation, CI, packaging, and containerized delivery for embedded robotics targets.
• Optimize on-device performance under real constraints including latency, throughput, memory footprint, thermals, and power.
• Debug complex issues across the stack spanning Python, C++, CUDA, drivers, and embedded Linux, including hard-to-reproduce device-specific failures.
• Integrate Warp components into robotics applications and frameworks, including ROS 2 and Isaac-based stacks, and work with partner teams to unblock adoption.
• Develop system-level testing, validation, and performance regression infrastructure for embedded targets.
• Collaborate with compiler, runtime, and kernel engineers to improve portability and performance across GPU architectures and embedded configurations.

Senior Software Engineer – NVIDIA Warp

• Lead bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads, setting the standard for how the team operates.
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks.
• Profile and optimize end-to-end workload performance across compute, memory, networking, and communication layers using tools such as Nsight Systems, NCCL tests, and custom microbenchmarks.
• Analyze scaling efficiency for distributed LLM workloads using data, tensor, pipeline, and expert parallelism across modern GPU clusters, and translate findings into concrete tuning guidance.
• Own root-cause analysis of complex failures — hangs, performance regressions, topology sensitivity in large distributed environments.
• Define and build the resilience and failure-attribution stack: detecting, triaging, and attributing node, fabric, and workload failures across the cluster at scale.
• Build repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams.
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.
• Mentor engineers, drive technical standards, and act as a force multiplier across the broader performance and infrastructure organization.

Senior Software Engineer, DGX Cloud AI Infrastructure

• Work together with our partners and customers to identify, analyze, and resolve complex performance bottlenecks across interconnected GPU, CPU, and networking systems.
• Complete and maintain robust performance benchmarking suites to stress-test high-performance clusters and establish performance baselines.
• Apply industry-standard performance tools to monitor hardware performance counters and extract deep system telemetry.
• Deeply investigate system and software configurations to find and fix subtle discrepancies that impact peak performance.
• Partner closely with internal engineering units and outside collaborators and customers to collectively develop solutions and boost infrastructure performance.

Senior Solutions Architect, AI Cluster Performance, Telemetry

• Drive planning and execution of multidisciplinary programs for AI Enterprise Workstation Solutions
• Support external OEM System Partners through system integration and production readiness
• Collaborate with multi-functional teams for Hardware/Software integration
• Lead and improve product development and software release processes