Senior Machine Learning Engineer, Accelerated Apache Spark

NVIDIA is hiring a Senior Machine Learning Engineer, Accelerated Apache Spark in Santa Clara, United States. It pays $224k-$357k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
Pay on this posting starts 13% higher than the typical machine learning engineer posting on Level.
The posting mentions PyTorch, TensorFlow and scikit-learn among its tools and skills.
Level classes it as a senior role.
NVIDIA has 2,700+ other open roles on Level.

AI in this role

PyTorchTensorFlowscikit-learnXGBoost

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.

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.
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!

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 224,000 USD – 356,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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.

How we rate this

Senior Machine Learning Engineer, Accelerated Apache Spark at NVIDIA rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior Machine Learning Engineer, Accelerated Apache Spark is an open job at NVIDIA.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $224k-$357k a year.
The role is based in US, CA, Santa Clara.
The job was posted on September 1, 2026.
Seniority: senior.
Applications are handled by NVIDIA. Apply on NVIDIA

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

PyTorchTensorFlowscikit-learnXGBoost

Also common in machine learning engineer jobs at this level

Deep learning AWS Kubernetes MLOps Fine-tuning Google Cloud

Pay

This job starts at: $224k
Typical machine learning engineer job: $198k

This posting starts 13% above the typical machine learning engineer job. See machine learning engineer jobs

Questions you could be asked

What's a project where you used PyTorch hands-on?
Walk me through how you've used TensorFlow in your day-to-day work.
What are the limits of scikit-learn that you've run into, and how did you work around them?
What's a project where you used XGBoost hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: PyTorch, TensorFlow, scikit-learn, and XGBoost. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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