Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,394 postings in the last year. Stated ranges, not salaries paid.
Santa Clara, CA, US
Live, first seen by us 71 days agoNVIDIA 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.Read the full posting
Employment type in the employer’s posting: FULL_TIMEFrom the employer’s posting
Design and implement ML solutions for performance prediction and optimization of Spark workloads on GPUs
Develop adaptive algorithms and systems that continuously improve Spark performance on GPUs
Develop AI-based agents and tools for fixing system issues and optimizing applications
Deploy complex ML solutions with key partners and customers in various environments
Provide technical mentorship and leadership to a team of engineers in ML
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.
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.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,394 postings in the last year. Stated ranges, not salaries paid.
Day 71
Similar jobs usually come down between day 8 and day 75.
Well past NVIDIA’s usual: its jobs come down by about day 32.
From 5,297 similar postings that came down, and 106 of NVIDIA’s. Coming down is not always a hire.
104 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
* 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!
* 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.
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:
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.
* 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.
* 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.
Ways to stand out from the crowd: * Understanding of the internal workings and architecture related to Apache Spark. * Familiarity with NVIDIA GPUs and CUDA.
* 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:
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New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
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.
Santa Clara, CA, US
Working pattern and location restrictions need checking in the full posting.
No clear work-authorization passage found. Eligibility is unconfirmed.