ZipRecruiter

Log In

1

Embedded Machine Learning Internship Jobs in Missouri

Jobtailor

Data Science Intern

California, MO · On-site

... internship * At least 6 months experience or academic work using open source programming languages for data analysis * At least 6 months of experience or academic work using machine learning ...

New

Smithfield Foods

Production OSU Intern

Jamesport, MO · On-site

$17.50/hr

... to learning, collaboration, and innovation, Smithfield offers challenging and rewarding careers ... Our internships are typically 10 weeks. Hourly Competitive Starting Pay - $17.50/hour Core ...

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

Smithfield Foods

Production OSU Intern

Jamesport, MO

$17.50/hr

... to learning, collaboration, and innovation, Smithfield offers challenging and rewarding careers ... Our internships are typically 10 weeks. Hourly Competitive Starting Pay - $17.50/hourCore ...

Jobtailor

AI Engineer

Troy, MO · On-site

$120 - $180/hr

Build, train, and evaluate machine learning models for classification, prediction, and simulation ... Internship, academic, or project experience building and deploying models or AI features end-to-end

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 ...

previous pagenext page

Showing results 21-40

All JobsEmbedded Machine Learning JobsEmbedded Machine Learning Internship JobsEmbedded Machine Learning Internship Jobs in Missouri

Embedded Machine Learning Internship information

What is an embedded machine learning internship?

An Embedded Machine Learning Internship is a temporary position designed for students or recent graduates to gain hands-on experience in developing and deploying machine learning algorithms on embedded systems. These internships typically involve working with hardware such as microcontrollers, sensors, or edge devices, and using specialized tools to optimize machine learning models for low-power and resource-constrained environments. Interns collaborate with engineers and data scientists to create efficient, real-world AI solutions that run directly on devices rather than relying on cloud computing. This role helps bridge the gap between theoretical machine learning concepts and practical implementation on embedded platforms.

What are some typical projects or tasks I might work on during an embedded machine learning internship?

During an Embedded Machine Learning Internship, you can expect to work on projects such as optimizing machine learning models to run efficiently on hardware with limited resources, integrating AI algorithms into embedded systems (like microcontrollers or IoT devices), and performing real-time data processing. You'll likely collaborate closely with software engineers and hardware designers to test models on physical devices, debug performance issues, and contribute to documentation. These experiences provide practical exposure to the challenges of deploying AI in real-world, resource-constrained environments and help build skills valuable for a future career in embedded AI.

What are the key skills and qualifications needed to thrive as an embedded machine learning intern, and why are they important?

To thrive as an Embedded Machine Learning Intern, you need a background in computer science, electrical engineering, or a related field with strong programming skills in C/C++ and Python, as well as foundational knowledge of machine learning algorithms. Experience with embedded systems development tools (such as ARM Cortex, Raspberry Pi, or Arduino), version control systems, and familiarity with ML frameworks like TensorFlow Lite or Edge Impulse is often required. Analytical thinking, problem-solving ability, and effective teamwork are vital soft skills for success in this role. These skills and qualities are crucial for efficiently developing, optimizing, and deploying machine learning solutions on resource-constrained embedded platforms.

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 Embedded Machine Learning Internship jobs in Missouri?

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

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

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

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

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

Embedded Machine Learning Internship jobs near you
Infographic showing various Embedded Machine Learning Internship job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% 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++
#J-18808-Ljbffr

Apply