At a Glance
- Tasks: Develop cutting-edge algorithms for NVIDIA's LPX inference and compiler stack.
- Company: Join NVIDIA, a leader in innovative technology and deep learning.
- Benefits: Competitive salary, health benefits, remote work options, and growth opportunities.
- Other info: Collaborative environment with opportunities to publish and present your work.
- Why this job: Be at the forefront of AI technology and make a real impact.
- Qualifications: MS or PhD in relevant field with strong software engineering skills.
The predicted salary is between 80000 - 100000 € per year.
NVIDIA is seeking engineers to develop algorithms and optimizations for our LPX inference and compiler stack. You will work at the intersection of large-scale systems, compilers, and deep learning, crafting how neural network workloads map onto future NVIDIA platforms. This is your chance to be part of something outstandingly innovative!
What You’ll Be Doing
- Build, develop, and maintain high-performance runtime and compiler components, focusing on end-to-end inference optimization.
- Define and implement mappings of large-scale inference workloads onto NVIDIA’s systems.
- Extend and integrate with NVIDIA’s SW ecosystem, contributing to libraries, tooling, and interfaces that enable seamless deployment of models across platforms.
- Benchmark, profile, and monitor key performance and efficiency metrics to ensure the compiler generates efficient mappings of neural network graphs to our inference hardware.
- Collaborate closely with hardware architects and design teams to feedback software observations, influence future architectures, and codesign features that unlock new performance and efficiency points.
- Prototype and evaluate new compilation and runtime techniques, including graph transformations, scheduling strategies, and memory/layout optimizations tailored to spatial processors.
- Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues.
What We Need To See
- MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent experience, with 6 years of relevant experience.
- Strong software engineering background with proficiency in systems level programming (e.g., C/C++ and/or Rust) and solid CS fundamentals in data structures, algorithms, and concurrency.
- Hands on experience with compiler or runtime development, including IR design, optimization passes, or code generation.
- Experience with LLVM and/or MLIR, including building custom passes, dialects, or integrations.
- Familiarity with deep learning frameworks such as TensorFlow and PyTorch, and experience working with portable graph formats such as ONNX.
- Solid understanding of parallel and heterogeneous compute architectures, such as GPUs, spatial accelerators, or other domain specific processors.
- Strong analytical and debugging skills, with experience using profiling, tracing, and benchmarking tools to drive performance improvements.
- Excellent communication and collaboration skills, with the ability to work across hardware, systems, and software teams.
- Ideal candidates will have direct experience with MLIR based compilers or other multilevel IR stacks, especially in the context of graph based deep learning workloads.
Ways To Stand Out From The Crowd
- Prior work on spatial or dataflow architectures, including static scheduling, pipeline parallelism, or tensor parallelism at scale.
- Contributions to opensource ML frameworks, compilers, or runtime systems, particularly in areas related to performance or scalability.
- Demonstrated research impact, such as publications or presentations at conferences like PLDI, CGO, ASPLOS, ISCA, MICRO, MLSys, NeurIPS, or similar.
- Experience with large-scale AI distributed inference or training systems, including performance modeling and capacity planning for multi rack deployments.
Senior Machine Learning Applications and Compiler Engineer, LPX in Cambridge employer: NVIDIA AI
NVIDIA is an exceptional employer, offering a dynamic work environment where innovation thrives at the intersection of deep learning and compiler technology. With a strong emphasis on employee growth, you will have access to cutting-edge projects and opportunities to collaborate with industry leaders, all while enjoying a culture that values creativity and teamwork in a vibrant location known for its tech-forward community.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Applications and Compiler Engineer, LPX in Cambridge
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at NVIDIA or similar companies. Attend meetups, webinars, or conferences related to machine learning and compilers to make connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving compiler development or deep learning frameworks. This can be a game-changer when you’re trying to stand out in interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your systems programming and compiler knowledge. Practice coding challenges and system design questions that relate to the role, so you can demonstrate your expertise confidently.
✨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, it shows you’re genuinely interested in being part of our innovative team at NVIDIA.
We think you need these skills to ace Senior Machine Learning Applications and Compiler Engineer, LPX in Cambridge
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with compiler development and deep learning frameworks. 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 this role. Share your passion for machine learning and compilers, and mention any specific experiences that relate to the job description. Let your personality shine through!
Showcase Your Projects:If you've worked on any interesting projects related to MLIR or compiler optimisations, make sure to include them in your application. We love seeing practical examples of your work, especially if they demonstrate your problem-solving skills and creativity.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining the StudySmarter team!
How to prepare for a job interview at NVIDIA AI
✨Know Your Stuff
Make sure you brush up on your knowledge of compilers, deep learning frameworks, and systems programming. Be ready to discuss your experience with LLVM, MLIR, and any relevant projects you've worked on. This is your chance to show off your technical expertise!
✨Showcase Your Problem-Solving Skills
Prepare to tackle some technical questions or coding challenges during the interview. Think about how you can demonstrate your analytical skills and debugging prowess. Practice explaining your thought process clearly, as this will help the interviewers see how you approach complex problems.
✨Collaborate Like a Pro
Since collaboration is key in this role, be ready to share examples of how you've worked with cross-functional teams in the past. Highlight any experiences where you influenced design decisions or contributed to team projects, especially those involving hardware and software integration.
✨Stay Current and Curious
Keep up with the latest trends in machine learning and compiler technologies. Mention any recent papers you've read or conferences you've attended. Showing your enthusiasm for continuous learning will set you apart and demonstrate your commitment to innovation in the field.
