At a Glance
- Tasks: Lead AI inference projects and optimise large-scale workloads on GPU clusters.
- Company: Join NVIDIA, a top tech employer known for innovation and diversity.
- Benefits: Competitive salary, comprehensive benefits, and a commitment to diversity.
- Other info: Engage with a vibrant community through workshops and hackathons.
- Why this job: Make a real impact in AI by solving complex challenges with cutting-edge technology.
- Qualifications: MS/PhD in relevant fields and 5+ years in neural networks optimisation.
The predicted salary is between 72000 - 88000 £ per year.
Job Requisition ID JR2024433 Job Category Sales Time Type Full time
We are looking for a Senior Solutions Architect with deep experience in large-scale production AI inference.
You will collaborate with top EMEA AI Natives, AI infrastructure providers, and enterprises deploying AI at scale.
As a trusted technical leader, you will ensure NVIDIA's inference stack achieves best performance, efficiency, and reliability.
You will address the industry's toughest AI inference challenges at the intersection of AI and high-performance computing, driving innovations in areas such as multi-node Mixture-of-Experts (Mo E) serving, interconnect-aware scheduling, memory-bound workload optimization, and next-generation inference architectures.
Your work will establish the technical direction for scalable, high-performance AI inference across the most demanding production environments.
- What You Will Be Doing
- Guide EMEA AI Natives customers in deploying and optimizing large-scale inference workloads on multi-node GPU clusters.
- Architect efficient inference pipelines for dense and sparse/latent Mo E models distributing workload among thousands of GPUs.
- Improve inference efficiency across quantization (INT4/FP8), speculative decoding, disaggregated prefill/decode, KV cache management, and Wide EP for large Mo E deployments.
- Collaborate with NVIDIA product teams (Dynamo, Tensor RT-LLM, NIXL) to accelerate customer success.
- Animate the AI inference developer’s community across EMEA through technical workshops, hackathons, and reference architectures.
- What We Need To See
- MS or Ph D in Computer Science, Engineering, High-Performance Computing, or equivalent professional experience.
- 5+ years of experience in Neural Networks inference optimization.
- Solid understanding of transformers inference optimization: quantization, disaggregated inference, speculative decoding, continuous batching, KV cache optimization.
- Practical experience in Mo E inference at scale: expert parallelism, Wide EP, all-to-all communication, routing overhead, and load balancing at scale.
- Ability to engage effectively with ML engineers, researchers, and systems architects at a deep technical level.
- Ways to stand out from the crowd
- Hands-on experience with NVIDIA Dynamo, NIXL, Grove, or emerging disaggregated inference tooling.
- Understanding of GPU memory hierarchies and high-speed interconnects (NVLink, Infini Band, RDMA, UCX).
- You have contributed to advanced AI lab or large scale AI infrastructure providers performing inference on thousands of GPUs.
- Published work or benchmarks in large-scale AI inference.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package.
As you plan your future, see what we can offer www. nvidiabenefits. com/
NVIDIA is committed to fostering a diverse 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.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
For
Poland: The base salary range is 292,500 PLN - 507,000 PLN.
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Senior Solutions Architect – Large Scale AI Inference employer: NVIDIA AI
NVIDIA is an exceptional employer, offering a dynamic work environment that fosters innovation and collaboration among talented professionals in the heart of the tech industry. With a strong commitment to employee growth, we provide extensive training opportunities and encourage the use of cutting-edge AI tools to enhance productivity and quality. Our inclusive culture, competitive salaries, and comprehensive benefits package make NVIDIA a rewarding place to advance your career while contributing to groundbreaking advancements in technology.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Solutions Architect – Large Scale AI Inference
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at NVIDIA AI or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to NVIDIA AI.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like NVIDIA AI.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like NVIDIA AI that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Senior Solutions Architect – Large Scale AI Inference
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at NVIDIA AI.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at NVIDIA AI and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at NVIDIA AI
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If NVIDIA AI uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.