Forward Deployed Architect

Posted 23 Days Ago
Be an Early Applicant
Hiring Remotely in Santa Clara, CA, USA
In-Office or Remote
224K-431K Annually
Expert/Leader
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Lead technical engagements enabling advanced AI infrastructure and integrations. Provide architecture direction, prototype and validate solutions, own strategic programs, develop reference architectures, and share field learnings to inform product and engineering.
Summary Generated by Built In

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Join our team and discover how you can build a lasting impact on the world.

NVIDIA is looking for a Forward Deployed Architect to provide technical leadership and strategic guidance across AI Accelerator engagements with AI Native organizations, NeoCloud Providers, and ISVs. You'll advise on architecture and integration, define what good looks like, and bring learnings back to inform the DSX product roadmap. This role is engaged when standard product capabilities are not enough and the work needs to be specialized. Curious about novel hardware before it has a playbook? Excited to define how new platforms get used at scale? Our team works alongside customers and partners on infrastructure problems no one has solved yet, helping teams adopt NVIDIA technology the right way and shaping how new AI workloads get deployed.

What you'll be doing:

  • Cross-Account Technical Leadership. Provide architectural direction across strategic engagements where standard capabilities are not enough and advanced implementation, optimization, or integration customization is needed.

  • Outcome-Focused Implementation. Help customers integrate the right components to deliver on their outcomes. Where DSX software fits, advise on adopting it the right way. Where it doesn't, help them succeed with the right alternative and bring the gap back to product and engineering.

  • Hands-On Technical Leadership. Dive into complex technical challenges hands-on when needed to solve critical problems, validate architectures, or prove out solutions.

  • Strategic Initiative Ownership. Lead technically demanding programs end to end, including third-party performance benchmarking across hardware and workloads.

  • Pattern Identification and Knowledge Sharing. Identify common challenges and solution patterns across engagements. Share findings with internal teams and the broader AI community.

  • Technical Standardization. Develop standardized approaches, reference architectures, and structured guidance rooted in patterns from successful engagements.

  • Cross-Functional Collaboration. Partner with product, engineering, and other customer-facing NVIDIA teams so what we learn in the field informs internal strategy and capabilities.

  • Strategic Architecture. Design technical strategies for advanced AI workloads (distributed training, large-scale inference, model and pipeline optimization, MLOps) that apply across multiple customers and partners.

  • New Hardware Enablement. Help develop new infrastructure patterns and playbooks for the latest NVIDIA hardware as it lands with customers and partners.

What we need to see:

  • Bachelors degree or equivalent experience.

  • 12+ years in technical roles such as solutions architecture, ML engineering, technical product management, or technical consulting across multiple customers or projects. Alternatively, 5+ years of specialist-level experience working at the frontier of AI infrastructure.

  • Strong technical leadership with the ability to guide teams and influence technical decisions without direct authority.

  • Systems thinking with the ability to understand customer outcomes and translate them into clear technical requirements and architectures.

  • Willingness to prototype, implement, validate, and troubleshoot hands-on when needed to solve critical problems or prove out approaches.

  • A solid technical foundation in the technologies AI infrastructure is built on, especially Linux systems administration.

  • A self-directed learner who can ramp on brand new technologies and unfamiliar technical domains independently.

  • Strong communication skills with the ability to engage technical teams, executives, and multi-functional collaborators.

Ways to stand out from the crowd:

  • Solutions architecture or technical consulting background across multiple customer engagements simultaneously, with experience bringing novel AI hardware or frameworks to production with frontier AI Native organizations, hyperscalers, NeoClouds, or ISVs.

  • A foundational cloud or distributed systems background built at hyperscaler scale.

  • A public technical voice: blog posts, talks, open-source contributions, or reference work that shows depth and opinion.

  • Hands-On Technical Expertise in one or more of: NVIDIA Stack (CUDA, NeMo, Triton, TensorRT, NIM, DGX Cloud, and the broader DSX software portfolio), Inference Systems (large-scale inference with frameworks like vLLM and SGLang, prefill-decode disaggregation, performance optimization across hardware), Training Systems (distributed training, model and pipeline optimization, open-source generative AI frameworks), Infrastructure (SLURM, Kubernetes, GPU scheduling, distributed computing frameworks, rack-scale systems, multiple CSP or NCP cloud environments), and Observability and Automation (CI/CD, infrastructure as code, GPU performance monitoring).

Even if your background doesn't match every line above, we'd love to hear how your experience applies!

With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the most desirable employers in the world. We have some of the most brilliant and talented people in the world working for us. If you are creative, autonomous and love a challenge, we want to hear from you!

#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 27, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills Required

  • Bachelor's degree or equivalent experience
  • 12+ years in technical roles (solutions architecture, ML engineering, technical product management, technical consulting) OR 5+ years specialist-level AI infrastructure experience
  • Strong technical leadership with ability to guide teams and influence without direct authority
  • Systems thinking: translate customer outcomes into technical requirements and architectures
  • Willingness to prototype, implement, validate, and troubleshoot hands-on
  • Solid technical foundation in AI infrastructure technologies, especially Linux systems administration
  • Self-directed learner able to ramp on new technologies and domains independently
  • Strong communication skills to engage technical teams, executives, and cross-functional collaborators
  • Solutions architecture or technical consulting background across multiple customer engagements, bringing novel AI hardware/frameworks to production
  • Foundational cloud or distributed systems background built at hyperscaler scale
  • Public technical voice: blog posts, talks, open-source contributions, or reference work
  • Hands-on expertise in NVIDIA Stack (CUDA, NeMo, Triton, TensorRT, NIM, DGX Cloud, DSX)
  • Experience with inference systems (e.g., vLLM, SGLang), prefill-decode disaggregation, and performance optimization
  • Experience with training systems: distributed training, model and pipeline optimization, generative AI frameworks
  • Infrastructure expertise: SLURM, Kubernetes, GPU scheduling, rack-scale systems, multi-cloud environments
  • Observability and automation experience: CI/CD, infrastructure as code, GPU performance monitoring

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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