Cuda Data Jobs

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8d ago

Principal Machine Learning Engineer, Accelerated Apache Spark

NVIDIA Santa Clara, CA

Santa Clara, CA

8d ago

$272k - $431k/yr Full time Python Big Data Bigdata

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Job Description

NVIDIA seeks a Principal Machine Learning Engineer to guide ML engineering on the GPU-accelerated Apache Spark program in Santa Clara, focusing on ML-driven performance prediction and optimization for Spark workloads. The role also involves building AI-based deployment tools and providing mentorship across environments. This onsite position offers a salary range of USD 272,000 to 431,250 per year.

Role overview

In this capacity, you will lead the ML engineering efforts for a GPU-accelerated Spark initiative, developing ML-based methods to forecast performance and optimize Spark workloads. You will additionally create AI-enabled tools and provide technical mentorship to enable deployment across diverse environments.

Responsibilities

Design and implement ML solutions for forecasting performance and optimizing GPU-accelerated enterprise Apache Spark workloads.
Develop advanced algorithms and adaptive systems to continually enhance Spark performance on GPUs.
Create AI-driven agents and tools to assist with diagnosing system issues and optimizing applications.
Collaborate with partners and customers to deploy complex ML solutions across varied environments.
Maintain deep domain expertise by staying current with the latest advances in ML systems and algorithms.
Provide technical leadership and mentorship in data science and ML for a team of engineers.

Requirements

Education: BS, MS, or PhD or equivalent experience in Machine Learning, Data Science, Computer Science, or a closely related field.
12+ years of professional experience designing, implementing, and productionizing high-quality ML/DL solutions.
5+ years of experience as a technical lead in ML model development.
Proven hands-on experience (2+ years) with large-scale data processing platforms such as Apache Spark.
Demonstrated ability to apply modern tooling and robust practices across all stages of building, deploying, and maintaining ML models.
Excellent programming skills in Python and related data science libraries (NumPy, pandas, scikit-learn, SciPy, PyTorch, TensorFlow).
Deep expertise in advanced ML methodologies, including LLMs/GenAI, reinforcement learning, and adaptive online ML systems.
Strong capability in feature engineering, feature importance assessment, and developing boosted tree models (e.g., XGBoost).

Technologies

Python
NumPy
pandas
scikit-learn
SciPy
PyTorch
TensorFlow
Apache Spark
XGBoost
Scala
Java
C++
CUDA

Benefits

Equity and benefits

Ways to stand out from the crowd

Understanding of the internal workings and architecture of Apache Spark
Familiarity with NVIDIA GPUs and CUDA
Experience coding in Scala, Java, and/or C++

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