NVIDIA

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

Senior Data Backend Engineer

Engineer

• Act as the on-site quality representative at the contract manufacturer, overseeing end-to-end quality processes for interconnect optical transceivers and CPO sub-assemblies
• Monitor and enforce manufacturing quality standards, including ESD controls, line setup, line audits, and process adherence
• Identify and escalate quality risks proactively
• Support New Product Introduction activities through product bring-up, troubleshooting, and coordination with cross-functional engineering teams across global locations
• Drive root cause analysis and corrective actions through 5D/8D reports for quality incidents
• Support continuous improvement efforts at the contract manufacturer site
• Collaborate with NVIDIA engineering, NPI, supplier quality, and manufacturing teams to ensure product reliability and successful production ramp

Senior NPI Engineer

Full-stack Engineer

• Architect, build, implement, and debug GPU power- and performance-management software, with particular emphasis on P-state management and DVFS
• Develop real-time controllers, policies, and mechanisms that dynamically optimize GPU performance, power, thermals, and energy efficiency under demanding data-center workloads
• Drive features across the full product lifecycle: 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 the power-management capabilities of next-generation processors
• Analyze complex interactions among workloads, clocks, voltages, power limits, thermals, telemetry, and system-level policies
• Lead the investigation and resolution of complex power, performance, stability, and reliability issues spanning 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 clear 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 – GPU Power Management

Platform Engineer

• Own a major Navigator data platform component and its roadmap, including architecture, interfaces, technical goals, and evolution
• Anticipate capacity, compatibility, and operational needs over a multi-year horizon
• Lead technical delivery across teams by clarifying requirements, breaking down work, managing dependencies and risks, and keeping stakeholders aligned
• Build batch and streaming ingestion, transformation, reconciliation, and serving processes for fleet, capacity, utilization, cost, scheduling, and operational telemetry
• Establish stable data models and agreements as sources, consumers, and scale evolve
• Direct creation and adoption of libraries, workflow/DAG abstractions, deployment tools, and standard implementation approaches
• Lead complex production investigations across pipelines, applications, SQL engines, Spark, storage, networks, and cloud services
• Define and implement testing, data-quality, reconciliation, lineage, SLO, and release-readiness standards
• Partner with security and infrastructure teams on trust boundaries, service identities, least privilege, secrets, environment isolation, and auditability
• Deliver well-modeled tables, APIs, automation, dashboards, and focused internal applications
• Guide design reviews, mentor engineers taking on larger ownership, and resolve technical disagreements
• Partner with leadership on priorities and explain how technical investments support DGXC objectives

Data and Platform Engineer

Security Engineer

• Own a core DGX Cloud security domain end to end, from design through production
• Embed with teams building GPU clusters, control planes, and services
• Join designs early, write code, and help land secure solutions
• Build secure paved roads that are easy for engineering teams to adopt by default
• Engineer systems that eliminate recurring classes of security risk
• Run deep-dive threat models across the substrate, orchestration layer, and tenancy boundary
• Test designs offensively and feed findings into baselines, guardrails, and detections
• Turn the DGX Cloud security bar into reference architectures, requirements, and golden paths
• Partner with DGX Cloud platform, production engineering, network security, and NVIDIA's central security organization
• Integrate secure practices into CI/CD and the hardware lifecycle
• Influence vertically organized teams through credibility and relationships rather than formal authority

Principal Engineer, Security Architecture – DGX Cloud

Robotics

• Develop algorithms, foundation models, and methods for robotic manipulation and/or loco-manipulation for industrial, scientific, and household applications
• Integrate methods into real-world robotic manipulation systems, including collaborative and industrial robot arms, mobile manipulators, humanoids, and dexterous hands
• Contribute to multi-person research projects across the robotics and machine learning stack
• Engage with the academic community through high-impact publications, conferences, workshops, and/or code releases
• Develop AI agents to accelerate research and engineering while ensuring correctness, reproducibility, and security
• Work on research areas including dexterous manipulation, mobile manipulation, humanoid loco-manipulation, multimodal robot learning, simulation, sim-to-real, real-to-sim, world models, real-world learning, robot foundation models, agentic robotics, robot learning theory, high-precision manipulation, reliable policy deployment, and scientific lab automation

Research Intern, Robotics

• Set the DGX Cloud security design standard across tenancy, GPU workloads, identity, supply chain, and isolation
• Create reference architectures, golden paths, and buildable security requirements
• Embed with engineering teams, learn the code, and help ship secure systems
• Drive multi-year architecture for common security infrastructure, including hardened baselines, patch SLOs, deploy-time policy, admission control, workload identity, supply-chain provenance, signing, attestation, detection, and response
• Eliminate recurring risks at their architectural root and improve paved roads, baselines, and detections
• Lead, hire, and grow a small team of Principal Engineers
• Serve as DGX Cloud's technical connection to NVIDIA's central security organization
• Partner with platform, production engineering, and network security teams
• Document security gaps and remediation needs for executives who own the risk
• Operate with influence rather than authority and do not personally block releases

Distinguished Engineer, Security Architecture – DGX Cloud

Data Engineer

• Own systems end to end from ambiguous customer and operational needs through architecture, implementation, deployment, observability, incident response, and ongoing support
• Design and maintain batch and streaming ingestion, transformation, reconciliation, and serving paths for fleet, capacity, utilization, cost, scheduling, and operational telemetry
• Build shared libraries, workflow and DAG abstractions, deployment tooling, data contracts, and paved-road platform patterns
• Engineer reliable distributed workloads and diagnose correctness and performance issues across applications, SQL engines, Spark jobs, storage systems, networks, and cloud services
• Build for retries, idempotency, backfills, schema evolution, and partial failure
• Apply least privilege, service identities, secrets management, access controls, environment isolation, auditability, and safe operational practices
• Establish automated tests, data-quality checks, lineage, freshness and completeness monitoring, actionable alerting, SLOs, and clear ownership
• Make trusted data usable through well-modeled tables, APIs, automation, dashboards, and focused internal applications
• Lead build reviews, communicate tradeoffs, mentor engineers, and improve architecture, testing, debugging, and operational practices

Senior Data Engineer – Platform

Developer Relations

• Serve as a trusted technical advisor to leading academic AI labs working on foundation models, LLMs, multimodal AI, reasoning, training, inference, and AI systems.
• Identify high-impact research workloads where NVIDIA software, systems, and accelerated computing platforms can advance model performance, scale, and efficiency.
• Engage principal investigators, postdocs, graduate researchers, and lab leadership to understand research goals, technical blockers, infrastructure needs, and collaboration opportunities.
• Track frontier AI research across papers, benchmarks, open-source projects, and academic labs to identify emerging trends and future platform opportunities.
• Partner with Research Account Managers, Solution Architects, Product, Engineering, and Business Development teams to support researcher adoption and long-term engagement.
• Translate academic feedback into actionable insights for product roadmaps, developer programs, education, and platform strategy.
• Support NVIDIA participation in major AI, ML, and systems research venues through technical content, workshops, university engagements, and lab-facing programs.

Developer Relations Manager – Higher Education and Research, Foundational AI

Solutions Engineer

• Oversee and prioritize the workflow of OEM technical teams supporting NVIDIA product integration, ensuring alignment with sales strategy and design win objectives
• Plan and coordinate efforts to integrate NVIDIA technologies into OEM platforms
• Lead prioritization and sequencing of workstreams delivering complete hardware and software solutions
• Resolve intricate customer issues, handle partner concerns, and maintain strong technical relationships
• Lead technical execution for major customer deployments, coordinating internal and external resources
• Participate in recruitment, lead interview panels, and prepare executive-level hiring justifications
• Provide input to business units, recommend procedural improvements, and contribute to OEM account strategy and roadmap planning
• Align technical and business teams in NPI, solution development, and OEM engagement
• Develop future-focused views of NVIDIA technology within OEM platforms and translate them into actionable plans
• Contribute to OEM strategy, mentoring, and process improvement

Manager, OEM Solution Architecture

SDET

• Design, develop, improve and maintain metrics and test sets that support large-scale KPIs and performance measurements for autonomous driving systems
• Collaborate across engineering, infrastructure, test engineering, and release to build and scale test solutions that address gaps in coverage and frameworks and mitigate risks to the project
• Build and drive improvements to tools that automate and scale test sets through metrics, data quantity, quality, and diversity, enabling a culture of test-driven development across the company
• Mentor and guide junior engineers, promoting a culture of technical excellence and collaborative problem solving
• Identify, troubleshoot, and resolve system-level issues that affect the performance and reliability of KPIs and simulation frameworks
• Build robust and scalable simulation infrastructure to test and validate self-driving technology over millions of virtual miles
• Integrate work across vehicle subsystems into NVIDIA's AV software platform while achieving or exceeding NVIDIA and automotive standards and guidelines

Senior Software Engineer – Test, Autonomous Vehicles

Machine Learning Engineer

• Design end-to-end solutions for perception and autonomous vehicle stacks to enable traffic signal detection across diverse driving environments, including complex intersections, rural roads, and highways
• Conduct applied research and development of innovative deep learning models for traffic light detection, traffic sign recognition, road marking detection, construction object detection, text recognition, and other traffic signal tasks
• Develop generalizable approaches to support diverse ODDs and country/region expansion
• Drive and prioritize data-driven development with large data collection and labeling teams
• Plan data collection and labeling priorities and optimize labeling efficiency to maximize data value
• Leverage data simulation and augmentation to solve extreme scenarios
• Productize perception solutions by meeting product requirements for safety, latency, and software robustness

Senior Machine Learning Engineer, Perception – Autonomous Driving

• Build, implement, and optimize tools to generate synthetic data for training DRIVE deep learning networks
• Develop lidar and radar sensor simulation workflows for NuRec reconstructed driving worlds and Cosmos-generated environments
• Develop Cosmos world models for controllable scenario generation, novel view synthesis, trajectory extrapolation, scene completion, quality triage, regression detection, and controllability evaluation
• Gather perception, planning, and deep learning network requirements and align them with synthetic data and sensor simulation capabilities
• Develop new tools and improve performance where capability gaps exist
• Develop dataset quality assessments and synthetic-real comparison procedures
• Evaluate sensor realism, annotation quality, distribution coverage, scenario diversity, and sim-to-real transfer
• Set up, profile, and supervise large-scale NuRec, Cosmos, and sensor simulation pipelines in data center or cloud environments
• Debug systems spanning sensors, reconstruction models, world models, simulation runtime, GPU workloads, distributed data services, and autonomous-driving workloads
• Collaborate with technical leaders in autonomous driving, NuRec, Cosmos, and sensor simulation

Senior Synthetic Data Engineer – Autonomous Driving

• Provide direct support to NVIDIA Enterprise customers to resolve or advance customer issues
• Work with engineering teams on customer issues, providing logs, reproduction, and other triage information
• Apply AI to create or update products and support tools
• Take ownership of customer issues from inception to resolution
• Document customer interactions to enhance the knowledge base
• Apply agentic AI skills to solving customer issues and software development
• Work directly with customers on NVIDIA platforms including GB200 and GB300
• Triage hardware platform issues and AI/ML workloads in large rack-scale datacenters
• Contribute to products and software tooling
• Occasionally work weekends and holidays to support customers

Senior Solution Engineer, Compute Systems

• Work as customer technical specialist to address customer requirements and technical challenges during the pre-sales activities of the Spectrum-X solution, with emphasis on whitebox switches
• Guide customers bringing up SONiC or their own NOS on NVIDIA Spectrum whitebox platforms — platform bring-up, feature enablement and debuggability
• Serve as the technical authority on the SAI layer and the NVIDIA Spectrum SDK: advise on API usage, feature mapping, ASIC capabilities and limitations, and help customers implement new features on top of them
• Debug complex networking and software issues across the full stack — customer NOS, SAI, SDK — and provide solutions to customers
• Work closely with R&D teams to solve customer issues and drive fixes upstream where relevant
• Participate in building the SW products roadmap by providing customer product requirements and feedback to engineering and marketing teams