Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing.
The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse.
NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.

NVIDIA

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NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.

• Address customer and partner optimization challenges by engaging directly with automotive OEMs and robotics associates to analyze, debug, and improve deep learning models on NVIDIA platforms
• Own performance benchmarking by driving efforts to achieve leading results on MLPerf Edge and industry benchmarks, defining methodology and ensuring reproducibility
• Evaluate emerging model architectures by analyzing DL architectures, including vision encoders, multi-modal VLMs, for compilation feasibility, memory footprint, and latency on target SOCs
• Collaborate across teams by partnering with compiler, runtime, and hardware teams to connect model-level insight with platform capabilities
• Deliver TensorRT and compiler-stack solutions for edge by creating and deploying inference solutions on Jetson, DRIVE, and GPU + ARM platforms for AV and robotics workloads.
• Develop Proofs of Readiness (PORs) and work closely with compiler team on Torch-TRT, MLIR-TRT, and related frameworks to bridge performance gaps.

Senior Systems Software Engineer – Deep Learning Solutions

Senior Engineer optimizing deep learning inference on edge hardware for autonomous vehicles and robotics at NVIDIA. Collaborating with automotive OEMs and addressing complex optimization challenges.

engineer

• Take a comprehensive view of the ASIC development lifecycle, identifying cross-stage bottlenecks and opportunities where automation and AI can improve predictability, convergence, and turnaround time.
• Identify, curate, and leverage real-time data to enable effective AI models and analytics, working with infrastructure teams to ensure scalable and secure data pipelines.
• Establish quantitative metrics to measure efficiency, quality, and cycle-time improvements; use data-driven insights to guide methodology decisions and prioritize investments.
• Serve as a technical catalyst within both the team and the wider company by sharing best practices, publishing internal guidelines, and mentoring engineers on emerging AI-enabled development techniques.
• Track advances in AI, EDA, and hardware design research, evaluating their applicability and guiding strategic bets that align with long-term product and technology roadmaps.

Senior ASIC Methodology Engineer

Senior ASIC Methodology Engineer at NVIDIA redefining AI hardware development methodology. Pioneering novel architectures and workflows in ASIC development.

• Extend and maintain hardware abstraction layers and core system libraries used across the platform.
• Design and implement drivers, runtimes, and data movement/aggregation pipelines supporting workload execution.
• Build and maintain runtime interfaces for launching, monitoring, and managing workloads.
• Improve platform reliability through automation, error reporting, diagnostics, and operational tooling.
• Debug and resolve complex sequencing, initialization, and runtime issues across multi-component systems.
• Partner cross-functionally with hardware engineering, compiler teams, and data center operations to bring features from prototype to production.
• Support new platform bring-up and NPI (New Product Introduction) efforts for new boards and silicon.
• Contribute to engineering excellence through documentation, tooling improvements, code reviews, and knowledge sharing.

Senior Systems Software Engineer

Develop and maintain hardware abstraction layers and runtime interfaces for NVIDIA’s computing platforms. Collaborate with cross-functional teams to enhance reliability and performance.

• Verify AI-related sophisticated ASIC designs & features with formal verification methods.
• Partner with architecture/RTL teams to specify properties, resolve deep design issues, and influence micro-architecture decisions.
• Leverage and unleash the power of formal verification to rigorously verify critical design properties and ensure compliance with specifications, as well as minimize spec ambiguities.
• Develop and implement advanced formal verification environments and methodologies for complex ASIC designs, including automated flows for scalability and efficiency.
• Train and coach junior engineers on formal techniques and standard processes; Help on methodology/FAQ documentation.

Senior Formal Verification Engineer

Senior Formal Verification Engineer verifying AI-related sophisticated ASIC designs at NVIDIA. Collaborating with teams to ensure correctness and developing advanced formal verification methodologies.

machine-learning-engineer

• Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.
• Define and implement mappings of large-scale inference workloads onto NVIDIA’s systems.
• Extend and integrate with NVIDIA’s SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.
• Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.
• Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.
• Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.
• Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues.

Senior Machine Learning Applications, Compiler Engineer

Senior Machine Learning Engineer at NVIDIA developing algorithms for inference and compiler stack. Collaborating across hardware and software teams for neural network workloads optimization.

ai-engineer

• Research, implement, and validate model architecture and algorithm changes that improve video generation fidelity, with emphasis on human-centric quality (identity preservation, anatomy, motion coherence, and interaction realism)
• Explore and prototype improvements across spatial multimodal modeling, modality alignment, flow-based or diffusion-based video generation, and neural rendering-inspired representations to improve controllability and long-horizon consistency
• Improve training and inference efficiency through architectural and post-training techniques (compute/memory optimizations, distillation, pruning, and compression)
• Define model training objectives that improve sim-to-real and real-to-sim generalization, especially for human motion, contact, and interaction dynamics across real-world and synthetic/simulation data
• Develop detailed, domain-specific benchmarks for evaluating world foundation models, especially generation and understanding world models that reason about video, simulation, and physical environments
• Translate research results into robust implementations like training code, production-grade checkpoints, model integrations, and demos that clearly showcase capability gains across teams

Senior AI Engineer, World Foundation Models

Senior AI Engineer at NVIDIA focused on developing generative models for high-fidelity video generation. Working on advanced AI systems that enhance real-world usability and visual quality.

NVIDIA

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6 jobs found

Senior Systems Software Engineer – Deep Learning Solutions

NVIDIA

Senior ASIC Methodology Engineer

NVIDIA

Senior Systems Software Engineer

NVIDIA

Senior Formal Verification Engineer

NVIDIA

Senior Machine Learning Applications, Compiler Engineer

NVIDIA

Senior AI Engineer, World Foundation Models

NVIDIA