At a Glance
- Tasks: Develop cutting-edge algorithms for NVIDIA's LPX inference and compiler stack.
- Company: Join NVIDIA, a leader in AI and deep learning technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with opportunities to publish and present your work.
- Why this job: Make a real impact on the future of AI and deep learning technologies.
- Qualifications: MS or PhD in relevant field with strong software engineering skills.
The predicted salary is between 70000 - 90000 £ 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.
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 (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:
- 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 Gruppe
NVIDIA is an exceptional employer for Senior Machine Learning Applications and Compiler Engineers, offering a dynamic work environment at the forefront of AI technology. With a strong emphasis on innovation, employees benefit from collaborative projects with hardware architects and opportunities for professional growth through cutting-edge research and development. The company fosters a culture of excellence and inclusivity, providing access to state-of-the-art resources and a vibrant community that encourages creativity and knowledge sharing.
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, and conferences related to machine learning and compilers. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving compiler development or deep learning frameworks. Share your work on GitHub or personal websites, and don’t forget to highlight any contributions to open-source projects.
✨Tip Number 3
Prepare for technical interviews by brushing up on your algorithms and data structures. Practice coding challenges on platforms like LeetCode or HackerRank. Make sure you can discuss your past projects and how they relate to the role you're applying for.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Tailor your CV and cover letter to highlight your experience with MLIR, LLVM, and any relevant projects. Let’s get you that interview!
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 is tailored to the role of Senior Machine Learning Applications and Compiler Engineer. Highlight your experience with compiler development, deep learning frameworks, and any relevant projects that showcase your skills in systems-level programming.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about working at NVIDIA and how your background aligns with the job description. Don’t forget to mention any specific experiences that relate to LPX inference and compiler stack.
Showcase Your Projects:If you've worked on any projects related to MLIR, LLVM, or deep learning frameworks, make sure to include them in your application. We love seeing practical examples of your work, especially if they demonstrate your ability to optimise performance and efficiency.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at NVIDIA Gruppe
✨Know Your Stuff
Make sure you brush up on your knowledge of compilers, deep learning frameworks, and the specific technologies mentioned in the job description. Be ready to discuss your experience with C/C++, LLVM, and MLIR, as well as any relevant projects you've worked on.
✨Showcase Your Problem-Solving Skills
Prepare to tackle some technical questions or problems during the interview. Think about how you would approach optimising a neural network workload or designing a new compiler pass. Use examples from your past work to illustrate your thought process.
✨Collaboration is Key
Since this role involves working closely with hardware architects and design teams, be ready to discuss your collaboration experiences. Share examples of how you've effectively communicated and worked with cross-functional teams to achieve project goals.
✨Stay Current and Curious
Demonstrate your passion for the field by discussing recent advancements in machine learning and compiler technologies. Mention any conferences you've attended or papers you've read, especially those related to spatial processors or performance optimisation.
