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Hourly Embedded Machine Learning Jobs in St Louis, MO

Chenega MIOS

ServiceNow Developer

Saint Louis, MO · On-site

$51.25 - $70.50/hr

Are you looking for a career where professional development is embedded in your employers core ... Collaborate with platform architects and engineering teams to integrate machine learning services ...

Chenega Corporation

ServiceNow Developer

Saint Louis, MO · On-site

$52.50 - $72/hr

Are you looking for a career where professional development is embedded in your employer's core ... Collaborate with platform architects and engineering teams to integrate machine learning services ...

Chenega MIOS

ServiceNow Developer

Saint Louis, MO · On-site

$51.25 - $70.50/hr

Are you looking for a career where professional development is embedded in your employers core ... Collaborate with platform architects and engineering teams to integrate machine learning services ...

Chenega Corporation

ServiceNow Developer

Saint Louis, MO

$52.50 - $72/hr

Are you looking for a career where professional development is embedded in your employer's core ... Collaborate with platform architects and engineering teams to integrate machine learning services ...

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All JobsHourly Embedded Machine Learning JobsHourly Embedded Machine Learning Jobs in St. Louis, MO

Hourly Embedded Machine Learning information

See St Louis, MO salary details

$68.1K

$149.1K

$169.2K

How much do hourly embedded machine learning jobs pay per year?

As of Jul 30, 2026, the average yearly pay for hourly embedded machine learning in St. Louis, MO is $149,124.00, according to ZipRecruiter salary data. Most workers in this role earn between $127,800.00 and $168,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Hourly Embedded Machine Learning Engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

How does an Hourly Embedded Machine Learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What is an Hourly Embedded Machine Learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What are popular job titles related to Hourly Embedded Machine Learning jobs in St. Louis, MO? For Hourly Embedded Machine Learning jobs in St. Louis, MO, the most frequently searched job titles are:
What job categories do people searching Hourly Embedded Machine Learning jobs in St. Louis, MO look for? The top searched job categories for Hourly Embedded Machine Learning jobs in St. Louis, MO are:
Hourly Embedded Machine Learning jobs near you

Performance Engineer Intern, Systems Software- Fall 2026

Nvidia Corporation

Saint Louis, MO • On-site

Full-time

Posted 24 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 246 rated software companies

Job description

NVIDIA is a worldwide technology company headquartered in Santa Clara, California. NVIDIA manufactures graphics processing units (GPUs), as well as system on a chip units (SOCs) for the expanding markets. Our work in visual computing, the art and science of computer graphics, has led to thousands of patented inventions, breakthrough technologies, deep industry relationships and a globally recognized brand. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers inventions such as artificial intelligence and autonomous cars.
You would join the team responsible for the maintenance, development, and execution of Desktop Gaming Performance testing in Linux and Windows environments for the world's fastest, power efficient GPUs. This job has a preferred duration of 8-12 months.
What you'll be doing:
  • Writing and maintaining containerized GPU accelerated workloads for the financial services industry, from deep learning training and inference, to portfolio optimization and backtesting.
  • Running, validating, and analyzing benchmarking models at scale on HPC clusters.
  • Visualizing performance data, building charts and dashboards using internal schemas and tooling.
  • Working closely with the latest and greatest in financial AI models and tooling to help build reference models for NVIDIA.

What we need to see:
  • Enrolled in a Bachelors program majoring in Computer Engineering, Software Engineering, Computer Science, or related field.
  • Desire to improve code quality by learning and applying computer science fundamentals, algorithms, and data structures.
  • Comfort with teamwork, collaboration, and a desire to reach across functional borders to develop new partnerships.
  • Active experience with Python.
  • Working comfort in a Linux command-line environment with version control.
  • Foundational understanding and interest of the machine learning lifecycle (training, evaluation, and inference).

Ways to stand out from the crowd:
  • Familiarity with PyTorch and/or training, testing, and evaluating machine learning models.
  • Experience with GPU computing or CUDA and libraries like cuOPT, CUTLASS, cuDNN, etc.
  • Exposure to workload orchestration and job schedulers (Kubernetes, Slurm).
  • Experience with containerized applications and resource management.
  • Interest in quantitative finance and applying performance data to real-world problems.

Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 20 USD - 71 USD.
You will also be eligible for Intern benefits.
Applications for this job will be accepted at least until July 10, 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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