Principal Machine Learning Engineer, Accelerated Apache Spark

NVIDIA offers equity and a benefits package, with an on-site机会 in Santa Clara, CA. This Principal Machine Learning Engineer role sits on the Accelerated Apache Spark team, focusing on ML driven performance prediction and optimization for GPU-accelerated Spark workloads. You will lead ML engineering efforts, mentor engineers, and help deploy AI based tooling across multiple environments, all within a culture that values technical excellence and collaboration. The position carries a salary range of USD 272,000 to 431,250 per year and requires onsite presence.

Responsibilities

• Design and implement machine learning solutions for predicting and optimizing performance of GPU-accelerated enterprise Apache Spark workloads.
• Develop advanced algorithms and adaptive systems to continually improve Spark performance on GPUs.
• Build AI based agents and tools to assist with diagnosing issues and optimizing applications.
• Partner with key stakeholders and customers to deploy complex ML solutions across diverse environments.
• Maintain deep domain expertise by tracking the latest advances in ML systems and algorithms.
• Provide technical mentorship and leadership in data science and machine learning for a team of engineers.

Requirements

• 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 as a 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 crafting, deploying, and maintaining ML models.
• Excellent programming skills in Python and related data science libraries (numpy, pandas, scikit-learn, scipy, pytorch, tensorflow).
• Deep experience with ML methodologies including LLM/GenAI, reinforcement learning, and adaptive online ML systems.
• Strong expertise 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

• 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++