Senior Full Stack Software Engineer, Marketing Platforms

NVIDIA•Us, CA

About The Position

NVIDIA is seeking a Senior Software Engineer with deep backend engineering experience to design, build, and operate platforms supporting NVIDIA Marketing. The role owns scalable backend services, distributed systems, and data integrations for customer profiles, consent, personalization, and campaign operations, from architecture and development through deployment and production operations. The engineer will build backend services and APIs, develop data integrations and responsive user interfaces, and lead architecture and integration decisions across partner teams. The role partners with product, design, and marketing to turn business needs into technical solutions, mentors engineers, and owns production readiness and operations. It also involves gaining specialized knowledge in applied AI and supporting AI-enabled features and agents as product demands evolve.

Requirements

Bachelor’s or master’s degree in Computer Science, Engineering, or a related technical field, or equivalent experience.
12+ years of software engineering experience, with substantial ownership of production backend services.
Deep knowledge of distributed systems and scalable architecture, including service decomposition, concurrency, consistency, asynchronous processing, caching, fault tolerance, and resilience.
Strong hands-on backend development skills in Java with Spring Boot or a comparable framework, including API development, authentication, authorization, and service-to-service integration.
Solid database knowledge, including SQL, relational and NoSQL data modeling, indexing, transactions, schema migrations, and performance tuning.
Experience operating production services on cloud infrastructure using containers, automated testing, Git, CI/CD pipelines, observability, and detailed debugging to maintain reliability and performance.
Hands-on frontend development skills in JavaScript/TypeScript and React or Angular, including component building, state management, API integration, and responsive, accessible interfaces.
Demonstrated technical leadership and end-to-end ownership, including turning ambiguous requirements into architecture and delivery plans, driving cross-team projects, communicating tradeoffs, and mentoring engineers.
Enthusiasm for AI-based applications and readiness to learn and assist in AI feature and agent development; prior AI or ML experience is not essential.

Nice To Haves

Experience scaling high-throughput or low-latency services through load testing, profiling, query optimization, caching, and capacity planning.
Experience with customer data platforms, identity resolution, consent management, audience activation, or personalization, including privacy practices for user data.
Experience with AWS or another major cloud platform, Kubernetes, messaging or streaming platforms, and complex service or database migrations.
Experience building enterprise web applications or reusable UI components, with automated checks and tools such as Selenium, Cucumber, Playwright, or Cypress.
Background with Python, AI-powered applications, LLMs, retrieval-augmented generation, tool-using agents, recommendations, or NVIDIA AI software.

Responsibilities

Architect, develop, and operate scalable backend services and distributed systems for customer profiles, consent, personalization, and campaign operations.
Build APIs, service boundaries, data models, and event-driven workflows, balancing consistency, availability, latency, efficiency, and operational complexity.
Develop dependable data integrations using relational and NoSQL databases, messaging systems, caching, and search, with attention to data integrity, schema evolution, and failure recovery.
Develop responsive, accessible user interfaces and integrate them with platform APIs to support data exploration, configuration, and marketing workflows.
Lead architecture decisions, align partner teams on interfaces and integration plans, and mentor engineers through building and code reviews.
Partner with product, design, and marketing teams to translate business needs into technical solutions.
Own production readiness and operations, including automated testing, CI/CD, deployments, observability, security, capacity planning, and incident resolution.
Modernize services through safe migrations and rollouts.
Develop specialized knowledge in applied AI and support AI-enabled features and agents by connecting platform APIs, data sources, and user workflows as product demands shift.