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Embedded Machine Learning Engineer Jobs in Missouri

Jobtailor

Machine Learning Engineer

California, MO · On-site

Collaborate with senior engineers and data scientists on model deployment * Conduct experiments and run machine learning tests * Build scalable ML pipelines * Deploy models into production ...

iHerb

Principal Machine Learning Engineer

California, MO · On-site

$205K - $230K/yr

Job Summary The Machine Learning Engineer will tackle challenging problems and create scalable machine learning systems and platforms that make an impact on millions of users. This role will work ...

Jobgether

$94K - $124K/yr

Our partner is looking for a Senior Machine Learning Engineer, Voice Agents based in Netherlands ... Experience deploying technology to embedded or robotics environments is beneficial. * A public ...

Jobtailor

Machine Learning Engineer II

California, MO · On-site

$87K - $119K/yr

Build machine learning systems and operations for personalization * Collaborate with applied science engineers to improve search and personalization platform quality, stability, and resilience

Bespoke Labs

Machine Learning Engineer

Columbia, MO · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Missouri

Embedded Machine Learning Engineer information

See Missouri salary details

$65.7K

$143.9K

$163.2K

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

As of Sep 10, 2026, the average yearly pay for embedded machine learning engineer in Missouri is $143,874.00, according to ZipRecruiter salary data. Most workers in this role earn between $123,300.00 and $162,300.00 per year, depending on experience, location, and employer.

What does an embedded machine learning engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are the key skills and qualifications needed to thrive as an embedded machine learning engineer?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What are some common challenges faced by embedded machine learning engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

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

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

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

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

Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer

California, MO • On-site

Jobtailor
See more jobs at Jobtailorexternal

Other

Re-posted 6 days ago

Job description

  • Assist in designing, developing, and implementing machine learning models and algorithms
  • Assist in developing and optimizing machine learning models
  • Preprocess and analyze datasets to ensure data quality
  • Collaborate with senior engineers and data scientists on model deployment
  • Conduct experiments and run machine learning tests
  • Build scalable ML pipelines
  • Deploy models into production environments
  • Stay updated with the latest advancements in machine learning
  • Enhance services through AI/ML solutions, drive business insights, and improve customer experiences
Requirements
  • 1+ years relevant experience and a Bachelor’s degree OR any equivalent combination of education and experience
  • Must have graduated within the past 12 months, or will be graduating by Spring 2027
  • Bachelor’s or Master’s degree in Computer Science or related field from an accredited college or university
  • Familiarity with ML frameworks such as TensorFlow or scikit-learn
  • Strong analytical and problem-solving skills
  • Visa sponsorship through PayPal is not available now or in the future
Core Competencies

Proficient in designing, developing, and implementing machine learning models and algorithms, with a strong focus on data preprocessing, model optimization, and deployment in production environments. Demonstrates analytical and problem-solving skills to enhance services through AI/ML solutions and drive business insights.

Highest-signal resume keywords
  • Machine Learning Model Development
  • Data Preprocessing and Analysis
  • Model Deployment
  • TensorFlow
  • Scikit-Learn
Hard Skills
  • Machine Learning
  • Data Analysis
  • Model Optimization
  • Algorithm Development
  • ML Pipeline Building
Soft Skills
  • Analytical Skills
  • Problem-Solving Skills
Industry Keywords
  • AI Solutions
  • Business Insights
  • Customer Experience
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