NVIDIASenior Deep Learning Solution Architect
NVIDIA is hiring a Senior Deep Learning Solution Architect in Beijing, China. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- On Level's scale, this solutions architect role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include vLLM, Sglang and Python.
- Level classifies it as a senior role.
- Level lists 2,700+ other open roles at NVIDIA.
AI in this role
Develop open-source inference frameworks, optimize LLM workloads, and drive R&D on compute performance for distributed training.
NVIDIA is leading company of AI computing. At NVIDIA, our employees are passionate about AI, HPC , VISUAL, GAMING. Our SA team is more focusing to bring NVIDIA new technology into difference industries. We help to design the architecture of AI computing platform, analysis the AI and HPC applications to deliver our value to customers, focusing on defining and solving computational challenges in LLM inference and training acceleration, as well as network communication and data transfer optimization.
What You'll Be Doing:
Contribute to the development of open-source inference frameworks such as SGLang and vLLM, including feature and operator development, performance optimization, and model support, in collaboration with the community.
Develop and optimize KV cache offloading frameworks for LLM workloads, supporting multi-level cache offloading and reuse across CPU, SSD, and remote storage to improve inference efficiency. (Team project: FlexKV)
Drive R&D on compute performance in distributed training, and explore methods and technologies for performance optimization.
Study computational challenges in machine learning systems, identify common needs and bottlenecks, and build example code, acceleration libraries, or frameworks accordingly.
What We Need to See:
Over 5 years working experience in the technology industry, with master’s degree or above in computer science, mathematics, electrical engineering, automation, or related fields.
Strong interest in accelerated computing, parallel computing, and heterogeneous computing, with the motivation to explore these areas in depth.
Solid programming skills, with a good understanding of data structures and computer systems fundamentals.
Strong learning agility, adaptability, and the ability to analyze, define, and independently explore technical problems.
Ways to Stand Out from the Crowd:
Familiarity with heterogeneous computing, distributed training, parallel computing, or other areas related to high-performance computing.
Experience in performance analysis, performance modeling, or performance optimization; contributions to open-source frameworks are a plus.
Strong ability to define new problems and explore solutions; candidates with independent PhD-level research experience are preferred.
Proficiency with AI coding tools.
With competitive salaries and a generous benefits package, we are widely considered to be one of the world’s most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous person with a real passion for technology, we want to hear from you.
How we rate this
Senior Deep Learning Solution Architect at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in solutions architect jobs at this level
Pay
- Typical solutions architect job
- $169k
This posting doesn't list pay. Use the typical solutions architect figure when pay comes up. See solutions architect jobs
Questions you could be asked
- Tell me about a project where deep learning was part of your work. What did you do?
- Tell me about a project where distributed training was part of your work. What did you do?
- Tell me about a project where llm inference was part of your work. What did you do?
- Tell me about a project where performance optimization was part of your work. What did you do?
- Walk me through how you've used vLLM in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Deep learning, Distributed Training, LLM Inference, Performance Optimization, and vLLM. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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