NVIDIA
Computer Graphics
SeniorSystemsEngineer,NeuralGraphics
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Systems Engineer, Neural Graphics at NVIDIA. Skills: graphics, AI, systems engineering, production systems, real-time pipelines. Take innovative world simulation techniques — like AlpaDreams — and drive them into production-ready, real-time pipelines integrated with downstream stacks. Own the end-to-end path from prototype to shipping product — performance optimization, system integration, quality bar”
What You'll Achieve.
shipping real products; product customers depend on; technology that actually works at scale
Industry & Context.
solving the hardest integration and performance challenges; solving the hard systems challenges — latency, memory, throughput
What They're Looking For.
Must Have
Degree in Computer Science, Computer Graphics, Machine Learning, or equivalent practical experience, 12+ years of experience with a sustained track record of shipping impactful products or technologies, Experience taking research concepts through the full lifecycle to production-quality systems, Systems-level thinking
Nice to Have
Hands-on work with world models, diffusion models, LLMs, neural radiance fields, differentiable rendering, or neural content generation in shipped products or production pipelines, Experience building scalable systems with AI agents, Non-traditional background bringing fresh, interdisciplinary approaches (e. g. , computational physics, materials science, game engine development), A track record of turning ambitious research into technology that actually works at scale, You think like a systems architect — you can articulate why a design decision was made, what alternatives were considered, and what trade-offs were accepted
What You'll Do.
Take innovative world simulation techniques — like AlpaDreams — and drive them into production-ready
real-time pipelines integrated with downstream stacks
Own the end-to-end path from prototype to shipping product — performance optimization
Solve the hard systems challenges — latency
throughput — that stand between a research demo and a product customers depend on
Make principled design trade-offs across the full stack — choosing the right level of complexity
identifying where a simpler approach wins
and understanding how decisions in one stage affect the rest of the pipeline
Full Job Description
Computer graphics is undergoing its most significant transformation since the invention of the GPU. AI and traditional rendering are converging, opening unprecedented possibilities for real-time visual experiences. At NVIDIA, our Neural Graphics team is where ground breaking techniques meet production systems used by millions of developers and creators worldwide. Through technologies like AlpaDreams — our neural world simulation system delivering neural-reconstruction-level fidelity in open worlds — we're solving the hardest integration and performance challenges at the frontier of AI and graphics. This is systems work — understanding the full problem, making sharp design trade-offs, and building something that holds together end-to-end under real-world constraints. We're looking for an outstanding engineer to be a driving force in this evolution. You'll bring deep expertise in graphics and AI to our production team, taking novel techniques and driving them through the hard last-mile engineering required to ship real products. You see research and engineering as the same work at different altitudes — and you're motivated by the systems optimization, integration challenges, and steadfast iteration that turn a promising idea into something millions of people use. If that sounds like you, this is an outstanding opportunity to shape the future of visual computing. ## ## ****What You 'll Be Doing:**** * Take innovative world simulation techniques — like AlpaDreams — and drive them into production-ready, real-time pipelines integrated with downstream stacks * Own the end-to-end path from prototype to shipping product — performance optimization, system integration, quality bar * Solve the hard systems challenges — latency, memory, throughput — that stand between a research demo and a product customers depend on * Make principled design trade-offs across the full stack — choosing the right level of complexity, identifying where a simpler approach wins, and understanding how
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