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Hourly Embedded Machine Learning Jobs in Missouri

SupportFinity™

Robotics Research Engineer

California, MO · On-site

$150 - $180/hr

... machine learning or advanced algorithms to robotics challenges, and hands‑on exposure to hardware, embedded systems, or electromechanical integration. Beyond technical depth, successful Robotics ...

New

CybSafe

Staff Data Scientist - Fraud & Risk

California, MO · On-site

$180 - $240/hr

Stay current with advancements in AI and machine learning, applying innovative approaches to real‑world problems. * Model Socure's embedded leadership competencies: continuous learning, effective ...

Lockton, Inc.

Full Lifecycle Data Engineer

Kansas City, MO · On-site

$111K - $134K/yr

... and machine learning. Key Responsibilities Data Ingestion & Integration • Build and maintain ... • Support embedded analytics or product-facing data features when needed Orchestration ...

Smithfield Foods

Production OSU Intern

Jamesport, MO · On-site

$17.50/hr

... to learning, collaboration, and innovation, Smithfield offers challenging and rewarding careers ... Hourly Competitive Starting Pay - $17.50/hour Core Responsibilities * Trains daily in a swine ...

Smithfield Foods

Production OSU Intern

Jamesport, MO

$17.50/hr

... to learning, collaboration, and innovation, Smithfield offers challenging and rewarding careers ... Hourly Competitive Starting Pay - $17.50/hourCore ResponsibilitiesTrains daily in a swine facility ...

Better Ventures

Firmware Engineer

California, MO · On-site

$160 - $200/hr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Background in aerospace, defense, robotics, automotive, or other safety‑critical embedded domains * Exposure to machine learning, signal processing, controls, or data analysis in a hardware context

New

AJM Packaging Corporation

Machine Technician

Joplin, MO · On-site

$17.75 - $23/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... has not wavered and is deeply embedded in its DNA. So, too, is the founding brothers ... learning and continuous improvement. There are no barriers to impede your progress here and no ...

Comprehensive Logistics

Line Operator

Wentzville, MO

$16 - $19.75/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... technology, machine learning, and process-driven execution to optimize workflows, eliminate inefficiencies, and ensure flawless delivery. More than a logistics provider, CLI is a true embedded ...

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All JobsHourly Embedded Machine Learning JobsHourly Embedded Machine Learning Jobs in Missouri

Hourly Embedded Machine Learning information

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.

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 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.

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 the most commonly searched types of Embedded Machine Learning jobs in Missouri?

The most popular types of Embedded Machine Learning jobs in Missouri are:

What are popular job titles related to Hourly Embedded Machine Learning jobs in Missouri?

For Hourly Embedded Machine Learning jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Hourly Embedded Machine Learning jobs in Missouri look for?

The top searched job categories for Hourly Embedded Machine Learning jobs in Missouri are:

What cities in Missouri are hiring for Hourly Embedded Machine Learning jobs?

Cities in Missouri with the most Hourly Embedded Machine Learning job openings:

Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Senior Machine Learning Perception Engineer - Fallback Driving System

Jobtailor

California, MO • On-site

$130 - $190/hr

Other

Posted yesterday

New

Job description

  • Design, train, and evaluate ML perception models for object detection, semantic/instance segmentation, tracking, and short-horizon prediction using camera, lidar, and radar data
  • Develop and maintain the secondary perception stack that enables the fallback autonomy system to bring the vehicle to a minimal risk condition
  • Define ML success metrics and drive systematic experimentation to improve performance
  • Analyze large-scale datasets, curate challenging scenarios, and develop data selection and labeling strategies
  • Implement efficient training and inference pipelines, including pruning, quantization, and distillation
  • Collaborate with software and infrastructure engineers to integrate models into production systems
  • Translate system requirements into ML model requirements, metrics, and validation criteria with Safety, Systems Engineering, and Product
  • Contribute to verification and validation through offline evaluation, simulation, hardware-in-the-loop, and on-road testing
  • Participate in code reviews, promote engineering best practices, and mentor other engineers
Requirements
  • BS, MS, or PhD in Machine Learning, Robotics, Computer Science, or a related technical field, or equivalent practical experience building ML perception systems
  • 3–5 years of experience developing ML solutions in perception, prediction, autonomous driving, or related domains
  • Strong experience with camera, lidar, and radar data, including preprocessing, synchronization, and fusion
  • Deep expertise in convolutional and transformer-based deep learning architectures for 2D/3D object detection, semantic and instance segmentation, multi-object tracking, and motion prediction
  • Proficiency in at least one major ML framework such as PyTorch, TensorFlow, or JAX
  • Python proficiency for model development, training, and analysis
  • Software engineering skills, including C++ or similar languages in large collaborative codebases
  • Ability to define ML metrics, design experiments, and systematically improve model performance and robustness
  • Experience deploying ML models on embedded or resource-constrained platforms, including optimization and real-time performance tuning is nice to have
  • Experience with AV/ADAS perception stacks, robotics, or ROS is nice to have
  • Familiarity with safety-critical systems and development practices is nice to have
  • Experience with large-scale data pipelines, labeling workflows, and ML experiment management is nice to have
Core Competencies

Demonstrates expertise in designing and evaluating ML perception models for object detection and tracking, utilizing camera, lidar, and radar data. Proficient in developing efficient training pipelines and collaborating with cross-functional teams to integrate models into production systems.

Highest-signal resume keywords
  • Machine Learning Perception Systems
  • Deep Learning Architectures
  • ML Frameworks (PyTorch, TensorFlow, JAX)
  • Python Proficiency
  • Data Pipeline Management
ATS Optimization Keywords Hard Skills
  • Object Detection
  • Semantic Segmentation
  • Instance Segmentation
  • Multi-Object Tracking
  • Motion Prediction
  • Model Evaluation
  • Model Optimization
  • Data Curation
  • Experiment Design
  • Model Deployment
Soft Skills
  • Collaboration
  • Mentoring
  • Problem-Solving
Industry Keywords
  • Autonomous Driving
  • ADAS
  • Safety-Critical Systems
  • Large-Scale Datasets
  • ML Experiment Management
Tools & Technologies
  • Camera Data
  • Lidar Data
  • Radar Data
  • ROS
  • C++
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