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- Tasks: Optimise and deploy deep learning models for high-performance inference on GPU platforms.
- Company: Join a leading tech company at the forefront of physical AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact in AI by working with cutting-edge technologies and talented teams.
- Qualifications: 5+ years in deep learning, strong Python and PyTorch skills, MSc or PhD preferred.
- Other info: Dynamic hybrid work environment with excellent career advancement opportunities.
The predicted salary is between 50000 - 85000 £ per year.
We are looking for a Senior Deep Learning Engineer to help bring Cosmos World Foundation Models from research into efficient, production-grade systems. You'll focus on optimizing and deploying models for high-performance inference on diverse GPU platforms. This role sits at the intersection of deep learning, systems, and GPU optimization - working closely with research scientists, software engineers, and hardware experts.
NVIDIA Cosmos is a platform purpose-built for physical AI, featuring powerful generative models. Developers use Cosmos to accelerate physical AI development for autonomous vehicles (AVs), robots, and video analytics AI agents by simulating and reasoning about the physical world.
What you'll be doing:
- Improve inference speed for Cosmos WFMs on GPU platforms.
- Effectively carry out the production deployment of Cosmos WFMs.
- Profile and analyze deep learning workloads to identify and remove bottlenecks.
What we need to see:
- 5+ years of experience.
- MSc or PhD in CS, EE, or CSEE or equivalent experience.
- Strong background in Deep Learning.
- Strong programming skills in Python and PyTorch.
- Experience with inference optimization techniques (such as quantization) and inference optimization frameworks, one of: TensorRT, TensorRT-LLM, vLLM, SGLang.
Ways to stand out from the crowd:
- Familiarity with deploying Deep Learning models in production settings (e.g., Docker, Triton Inference Server).
- CUDA programming experience.
- Familiarity with diffusion models.
- Proven experience in analyzing, modeling, and tuning the performance of GPU workloads, both inference and training.
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 221,250 PLN - 383,500 PLN for Level 3, and 292,500 PLN - 507,000 PLN for Level 4.
Senior Deep Learning Engineer employer: Nvidia
Contact Detail:
Nvidia Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Deep Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that Senior Deep Learning Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your deep learning projects, especially those involving GPU optimisation. We recommend sharing this on GitHub or your personal website to catch the eye of hiring managers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and PyTorch skills. We suggest doing mock interviews with friends or using platforms that focus on coding challenges to get comfortable with the types of questions you might face.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior Deep Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in deep learning and GPU optimisation. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to tell us why you’re the perfect fit for the Senior Deep Learning Engineer position. Share your passion for AI and how your background makes you a great match for our team.
Showcase Your Technical Skills: We’re looking for strong programming skills in Python and PyTorch, so make sure to mention any relevant projects or experiences. If you’ve worked with inference optimisation techniques, let us know!
Apply Through Our Website: To make sure your application gets to us, apply directly through our website. It’s the best way to ensure we see your application and can get back to you quickly!
How to prepare for a job interview at Nvidia
✨Know Your Deep Learning Stuff
Make sure you brush up on your deep learning knowledge, especially around inference optimisation techniques like quantisation. Be ready to discuss your experience with frameworks like TensorRT and how you've applied them in real-world scenarios.
✨Show Off Your Programming Skills
Since strong programming skills in Python and PyTorch are a must, prepare to showcase your coding abilities. You might be asked to solve a problem on the spot, so practice coding challenges related to deep learning and GPU optimisation.
✨Understand the Cosmos Platform
Familiarise yourself with NVIDIA's Cosmos platform and its applications in physical AI. Being able to discuss how you've used or could use this platform for deploying models will definitely set you apart from other candidates.
✨Prepare for Technical Questions
Expect technical questions that dive deep into your experience with GPU workloads and performance tuning. Think of specific examples where you've identified bottlenecks and how you resolved them, as this will demonstrate your hands-on expertise.
