NVIDIA is looking for a Machine Learning (ML) Engineer to join the GPU accelerated Apache Spark team. Apache Spark is the most popular data processing engine in data centers for running large scale workloads for ETL, SQL, and ML/DL model training and inference pipelines, spanning many domains and use cases. NVIDIA GPUs offer a promising avenue for significantly speeding up and/or lowering the cost of running Apache Spark applications at massive scales. You will work with the open source community to accelerate Apache Spark with GPUs. You will apply the latest ML/AI methods to empower enterprises to migrate Spark workloads onto GPUs at scale.
What you’ll be doing:
Design and implement machine learning solutions for performance prediction and optimization of GPU accelerated enterprise Apache Spark workloads.
Develop advanced algorithms and adaptive systems to continuously improve the performance of Apache Spark workloads on GPUs.
Develop AI-based agents and tools to assist with fixing system issues and application optimization.
Collaborate with key partners and customers on the deployment of complex machine learning solutions in various environments.
Maintain deep domain expertise by knowing the latest published advances in ML systems and algorithms.
Provide technical mentorship and leadership in data science and machine learning to a team of engineers.
What we need to see:
BS, MS, or PhD or equivalent experience in Machine Learning, Data Science, Computer Science or a closely related field.
12+ years of professional experience in designing, implementing, and productionizing high-quality ML/DL solutions.
5+ experience as technical lead in ML model development.
Proven hands-on experience (2+ years) with large-scale data processing platforms, such as Apache Spark.
Proven ability to employ modern tooling and sound techniques for all aspects of crafting, deploying, and maintaining machine learning models.
Excellent programming skills in Python and Python data science related libraries like numpy, pandas, scikit-learn, scipy, pytorch, and tensorflow.
Deep experience with sophisticated ML methodologies, including LLM/GenAI, reinforcement learning, and adaptive, on-line ML systems.
Strong expertise in feature engineering, feature importance assessment, and developing boosted tree model solutions (e.g., XGBoost).
Ways to stand out from the crowd:
Understanding of the internal workings and architecture related to Apache Spark.
Familiarity with NVIDIA GPUs and CUDA.
Experience coding in Scala, Java, and/or C++.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most experienced and dedicated people in the world working for us. If you are passionate about what you do, creative and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
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.Skills Required
- BS, MS, or PhD in Machine Learning, Data Science, Computer Science or related field
- 12+ years in ML/DL solutions
- 5+ years as technical lead in ML model development
- 2+ years hands-on with Apache Spark
- Proficient in Python and data science libraries
- Deep experience with ML methodologies including reinforcement learning
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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