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# Senior Machine Learning Engineer, Accelerated Apache Spark

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $224,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Algorithm Design, Big Data, C++ (Programming Language), Nvidia CUDA, Computer Programming, Databases, Data Centers, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, NumPy, Open Source Technology, Tensorflow, SciPy, SQL Databases, Reinforcement Learning, Data Processing, Graphics Processing Unit (GPU), Computer Network Operations, Feature Engineering, Pytorch, Delivery Pipeline, Large Language Models, Apache Spark, Pandas, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-accelerated-apache-spark-santa-clara-ca--d90d2340-8aed-419a-9117-1ce72fce8c88

## About the Role

Machine Learning, Apache Spark, Machine Learning, Apache Spark, GPU (Graphics Processing Unit), Use Cases, Data Processing, Network Operations Center, Open Source, Database Extract Transform and Load (ETL), * 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.
 * 3+ 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!

## Description

NVIDIA is looking for a Machine Learning Engineer to join the GPU accelerated Apache Spark team.

Apache Spark is the most popular data processing engine in data centers for running massive 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 very large Apache Spark applications. 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.