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Embedded Machine Learning Engineer Jobs in Saint Louis, MO

Bespoke Labs

Machine Learning Engineer

Saint Louis, MO · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Brooksource

MLE II

Saint Louis, MO · On-site

$50 - $55/hr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Machine Learning Engineer II Remote (U.S.) Remote Role Compensation: $50 - $55 per hour ABOUT THE ROLE Brooksource is partnering with a Fortune 50 healthcare organization to hire a Machine Learning ...

Varsity Tutors

Machine Learning Tutor

Saint Louis, MO · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Booz Allen Hamilton

Agentic AI Developer

O Fallon, IL · On-site

$99K - $225K/yr

  • Medical

  • Life

  • Retirement

  • PTO

As a machine learning engineer on our Defense Technology team, you'll train, test, deploy, and maintain models that learn from data to create real-world, mission-critical impacts. In this role, you ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Saint Louis, MO

Embedded Machine Learning Engineer information

See Saint Louis, MO salary details

$68.1K

$149.1K

$169.2K

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

As of Aug 20, 2026, the average yearly pay for embedded machine learning engineer in Saint 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 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 Saint Louis, MO?

For Embedded Machine Learning Engineer jobs in Saint Louis, MO, the most frequently searched job titles are:

What job categories do people searching Embedded Machine Learning Engineer jobs in Saint Louis, MO look for?

The top searched job categories for Embedded Machine Learning Engineer jobs in Saint Louis, MO are:

Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer 727

Freedom Technology Solutions Group

Saint Louis, MO

Full-time

Re-posted 2 days ago

Job description

Freedom Technology Solutions Group is seeking a Machine Learning Engineer to develop, deploy, and optimize production AI/ML capabilities supporting mission-critical geospatial and intelligence systems. You will work at the intersection of software engineering, cloud architecture, and data science to build scalable machine learning pipelines capable of operating within secure government environments.

This is a hands-on engineering position focused on moving models from research into reliable production systems.


Responsibilities:

  • Da
  • Design, train, validate, and deploy machine learning models
  • Build production inference pipelines
  • Develop feature engineering workflows
  • Optimize model performance and resource utilization
  • Implement MLOps pipelines supporting continuous integration and deployment
  • Build scalable APIs exposing AI capabilities
  • Monitor model drift and operational performance
  • Collaborate with Data Scientists and Software Engineers
  • Deploy AI workloads into AWS cloud environments
  • Support computer vision, NLP, and geospatial AI initiatives
  • Collaborate with architects, data scientists, and mission stakeholders to gather, document, and refine customer requirements, including data mapping and integration needs
  • Assist in implementing integration solutions in collaboration with development team members
  • Facilitate communication between stakeholders to ensure timely and effective requirements execution
  • Ensure activities align with established processes, standards, and mission objectives
  • Contribute to documentation of processes, procedures, integration patterns, and lessons learned


Key Technologies

  •  A
  • Python
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • Hugging Face
  • MLflow
  • Docker/Podman
  • Kubernetes/EKS
  • ECS
  • Lambda
  • SageMaker
  • GitLab CI/CD
  • Linux
  • PostgreSQL/PostGIS, Aurora, Oracle (w/Spatial)
  • Redis, Elasticache
  • GDAL, Rasterio, OGR

Required Qualifications

  • Active TS/SCI clearance (eligible for CI Poly)
  • 1-3(Junior), 3-7(Journeyman), 8-11 (Senior), >12 (Principal) years of experience in software development, system integration, or technical support roles
  • Experience working directly with customers or stakeholders in a technical or mission environment
  • Strong communication and coordination skills across technical and non-technical teams
  • Experience gathering and documenting requirements
  • Ability to manage multiple tasks and priorities in a dynamic environment
  • Familiarity with Agile development practices
  • Experience using GitLab or similar tools for collaboration and tracking


Desired Qualifications

  • Experience deploying production AI systems
  • Experience with computer vision
  • Experience with large language models
  • Geospatial AI experience
  • AWS AI services
  • Experience processing satellite imagery
  • Familiarity secure data movement environments
  • Experience working with enterprise service processes such as Service+
  • Development or scripting experience (Python, JavaScript, or similar)
  • Geospatial/GIS development a plus
  • Experience with data mapping or integration workflows (using JSON or other object notation)
  • Familiarity with operational dashboards and metrics reporting
  • Experience supporting customer requirement implementation and/or system integration efforts

Apply