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Deep Learning Compiler Engineer

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.We are looking for outstanding ML/DL compiler engineers to join the team and develop groundbreaking technologies in machine learning compilers and AI systems. We build innovative AI compiler solutions that works together with NVIDIA's software stack to provide comprehensive acceleration for modern machine learning models. As a member of the team, you will develop innovative AI compiler technologies for NVIDIA's hardware architecture. You will develop new ML/DL compiler abstractions, build runtimes, and ML/DL -compiler driven system solutions to accelerate large language models, agents and other high-impact machine learning workloads. As part of this role, you will be building a close technical relationship with internal NVIDIA software and hardware teams on push the latest developments to NVIDIA's product.What You'll Be Doing• Innovate and develop new machine learning compiler and systems technologies• Design, implement, and optimize compilers for high impact AI workloads• Co-design learning system solutions with current and future ML compiler and algorithm technologies.• Collaborate closely with other engineering teams at NVIDIA to build high impact solutions for machine learning accelerationWhat We Need To See• Bachelor's degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); MS or PhD are preferred• 6+ years (academic/ industry) experience with ML/DL systems development preferable for compilers• Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX etc.)• Strong python and C/C++ programming skillsWays To Stand Out From The Crowd• Expertise in AI frameworks such as PyTorch, TensorFlow, and ONNX• Expertise in machine compilers (e.g. Apache TVM, MLIR)• Strong experience in GPU performance optimizations• Strong experience machine learning systems research and productionization• Open source project ownership or contributionsWith competitive salaries and a generous benefits package, we are widely considered to be one of the technology 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 unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.The base salary range is 180,000 USD - 339,250 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.You will also be eligible for equity and benefits . NVIDIA accepts applications on an ongoing basis.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.JR1993129
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$180000K
$339250K

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What You Should Know About Deep Learning Compiler Engineer, NVIDIA

NVIDIA is on the lookout for a talented Deep Learning Compiler Engineer to join our innovative team in the United States. With over 25 years of pioneering work in computer graphics and accelerated computing, we are now at the forefront of harnessing AI to revolutionize the computing landscape. As a Deep Learning Compiler Engineer, you will play a crucial role in crafting groundbreaking technologies that enhance machine learning compilers and AI systems. Your work will involve developing AI compiler solutions in tandem with NVIDIA's robust software stack to optimize modern machine learning models. You will have the opportunity to engage in creating new compiler abstractions and runtimes while collaborating closely with various internal teams to push the envelope in our product offerings. If you have a passion for optimally designing and implementing compilers tailored for high-impact AI workloads, this role is perfect for you. At NVIDIA, we believe in a diverse and collaborative work environment that empowers creativity and innovation, so come and see how you can leave a lasting impact on the future of technology!

Frequently Asked Questions (FAQs) for Deep Learning Compiler Engineer Role at NVIDIA
What are the primary responsibilities of a Deep Learning Compiler Engineer at NVIDIA?

As a Deep Learning Compiler Engineer at NVIDIA, your main responsibilities will include developing innovative machine learning compiler technologies, optimizing compilers for AI workloads, and collaborating with other engineering teams to deliver impactful solutions. You'll also design and implement compiler abstractions and runtimes that are tailored to accelerate diverse machine learning environments.

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What qualifications are needed for the Deep Learning Compiler Engineer position at NVIDIA?

To qualify for the Deep Learning Compiler Engineer role at NVIDIA, you should have at least a bachelor's degree in Computer Science or a related field, although an MS or PhD is preferred. Additionally, six or more years of experience in ML/DL systems development, especially with compilers, along with strong programming skills in Python and C/C++, are essential.

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How does NVIDIA support ongoing learning and development for Deep Learning Compiler Engineers?

NVIDIA is committed to the professional growth of its Deep Learning Compiler Engineers by offering numerous opportunities for continuous learning. You can participate in cutting-edge research projects, increase your expertise in machine learning frameworks, and contribute to open-source initiatives. Collaborating with talented peers and attending conferences are also key components of our supportive work environment.

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What programming languages and tools are fundamental for the Deep Learning Compiler Engineer position at NVIDIA?

For the Deep Learning Compiler Engineer role at NVIDIA, proficiency in programming languages such as Python and C/C++ is crucial. Familiarity with deep learning frameworks like PyTorch, TensorFlow, and ONNX, along with experience using machine compiler tools such as Apache TVM and MLIR, will significantly enhance your effectiveness in this role.

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What are the benefits of working as a Deep Learning Compiler Engineer at NVIDIA?

Working as a Deep Learning Compiler Engineer at NVIDIA comes with competitive salaries, a generous benefits package, and the chance to be part of a dynamic team that is shaping the future of AI technology. Additionally, NVIDIA prides itself on a diverse and inclusive culture that promotes creativity and allows for substantial career growth.

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Common Interview Questions for Deep Learning Compiler Engineer
Can you describe your experience with deep learning frameworks as a Deep Learning Compiler Engineer?

In interviews, you should highlight your hands-on experience with frameworks such as TensorFlow, JAX, or PyTorch. Discuss specific projects where you optimized models or contributed to compiler technologies and emphasize your familiarity with the framework features that facilitate compiler integration.

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What strategies do you employ to optimize compilers for machine learning workloads?

When answering this question, provide examples of optimization techniques you've used, such as graph optimization, memory management strategies, or leverage profiling tools for performance tuning. Clearly explain how these strategies resulted in significant speed-ups or efficiency improvements.

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How do you approach collaboration with other engineering teams?

Collaborating with others is key in this role. Share examples of successful teamwork experiences where you engaged with software and hardware groups to create comprehensive solutions and how you ensured effective communication across different technical disciplines.

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What is your understanding of the current trends in deep learning compilers?

In your response, discuss emerging trends like the rise of modular and flexible compilers, and the utilization of AI-based optimizations. Stay informed about industry advancements and mention how you plan to keep up with these trends through continuous learning.

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Can you provide an example of a complex machine learning problem you solved?

Detail an instance where you had to troubleshoot a challenging machine learning issue. Explain the context, the steps you took to address the problem, the outcome, and what you learned from the experience.

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How do you prioritize tasks when working on multiple projects?

Discuss your method for prioritizing tasks effectively. Illustrate how you assess the urgency and importance of tasks, and how you communicate with stakeholders to manage expectations while maintaining quality across projects.

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What programming methodologies do you prefer when developing compilers?

Share the programming methodologies you employ, such as Agile or Test-Driven Development (TDD). Explain why these methods are beneficial in compiler development and how they help in maintaining quality and adaptability as projects evolve.

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How do you ensure that your compilers remain relevant with rapidly changing AI technologies?

Address how you stay engaged with advancements in AI and compiler technologies. Mention leveraging open-source communities, attending conferences, and contributing to collaborative projects as ways to remain knowledgeable and forward-thinking.

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What performance metrics do you consider when evaluating compiler efficiency?

Discuss key performance metrics such as compilation time, runtime efficiency, memory usage, and scalability. Offer insights on how you analyze these metrics to guide optimization efforts and improve overall performance.

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Why do you want to work at NVIDIA as a Deep Learning Compiler Engineer?

Express your passion for cutting-edge technology and the innovative culture at NVIDIA. Highlight how the mission and values of NVIDIA align with your career goals, particularly in shaping the future of AI and deep learning.

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NVIDIA is a publicly traded, multinational technology company headquartered in Santa Clara, California. NVIDIA's invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, and ignited the era of modern AI.

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CULTURE VALUES
Customer-Centric
Mission Driven
Inclusive & Diverse
Rise from Within
Diversity of Opinions
Work/Life Harmony
Growth & Learning
Transparent & Candid
BENEFITS & PERKS
Medical Insurance
Paid Time-Off
Maternity Leave
Mental Health Resources
Equity
Child Care stipend
Paternity Leave
WFH Reimbursements
Flex-Friendly
Dental Insurance
Vision Insurance
Life insurance
Health Savings Account (HSA)
Flexible Spending Account (FSA)
401K Matching
Military leave
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Full-time, on-site
DATE POSTED
December 21, 2024

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