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Embedded Machine Learning Engineer Jobs in Tennessee

Machine Learning Tutor

Memphis, TN · 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 ...

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

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

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

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

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

Software Engineer- Embedded/Firmware

Knoxville, TN · On-site

$91K - $125K/yr

... Engineer (Embedded/Firmware) in Knoxville, Tennessee. Location: Knoxville TN Salary: Highly ... machines, together with veteran business leaders experienced in scaling companies and ...

... Engineer (Embedded/Firmware) in Knoxville, Tennessee. Location: Knoxville TN Salary: Highly ... machines, together with veteran business leaders experienced in scaling companies and ...

Showing results 41-60

Embedded Machine Learning Engineer information

See Tennessee salary details

$63.5K

$139.2K

$157.9K

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

As of Aug 18, 2026, the average yearly pay for embedded machine learning engineer in Tennessee is $139,213.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,400.00 and $157,000.00 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 cities in Tennessee are hiring for Embedded Machine Learning Engineer jobs?

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

Staff Software Engineer / Machine Learning Engineer - Radiology

St. Jude Children's Research Hospital, Inc.

Memphis, TN • On-site

$104 - $186.16/hr

Other

Re-posted 12 days ago


St. Jude Children's Research Hospital rating

8.6

Company rating: 8.6 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

43rd of 1,060 rated hospitals


Job description

Machine Learning Engineer

We are seeking a highly motivated and experienced Machine Learning Engineer to develop advanced machine learning (ML), deep learning (DL), and foundational AI models for medical imaging. This role focuses on building robust algorithms for segmentation, quantification, and detection across CT, MRI, and X‑ray. This position sits at the center of a well‑resourced, data‑rich research environment with established infrastructure for multi‑institutional data aggregation, curation, and large‑scale annotation. St. Jude Children’s Hospital has incredible high‑performance computing resources. The lab leverages curated datasets from diverse sources, dedicated annotation teams, and external engineering support, enabling this role to focus on high‑impact model development, validation, and clinical translation. Many projects are designed with a path toward regulatory clearance via FDA’s 510(k) or De Novo pathways, and the successful candidate will work closely with regulatory and quality experts to support reproducible, well‑documented, and clinically deployable AI solutions. This role offers a unique combination of academic productivity (authorship opportunities) and real‑world impact through translation into clinical practice.

Key Responsibilities
  • Develop, train, and validate state‑of‑the‑art ML/DL models for segmentation, quantification, and detection across CT, MRI, and X‑ray
  • Design and implement 2D and 3D model architectures (CNNs, transformer‑based, and foundational models)
  • Build scalable pipelines for data preprocessing, model training, evaluation, and deployment
  • Develop quantitative imaging methods (e.g., volumetrics, density measurements, biomarker extraction)
  • Leverage curated, multi‑institutional datasets to ensure model generalizability and robustness
  • Collaborate with radiologists and engineering teams to define clinically meaningful outputs
  • Produce regulatory‑grade documentation for datasets, model development, validation, and performance
  • Ensure reproducibility and traceability of experiments (data, model, and code versioning)
  • Work collaboratively with regulatory and quality experts to support FDA 510(k) and De Novo submissions, including providing technical documentation and validation evidence
  • Contribute to software quality and security practices, including supporting activities such as vulnerability assessment and penetration testing in collaboration with cybersecurity and regulatory teams
  • Utilize modern AI‑assisted development tools (e.g., LLM‑based coding agents) to accelerate development and improve code quality
  • Participate in team‑based development practices (code reviews, Git, testing frameworks)
  • Support manuscripts, grants, and technical reporting
Minimum Education and/or Training

Bachelor’s degree in computer science, data science, information science, business, or related field. Master’s degree preferred.

Minimum Experience

Minimum Requirement: Bachelor’s degree with 5+ years of experience required. Experience Exception: Master’s degree with 3+ years of experience.

Experience with programming languages, databases, and software development lifecycle. Experience with the position‑specific technical stack preferred. Experience with the position‑specific scientific domain preferred. Proven performance in earlier role/comparable role.

Preferred Qualifications
  • 3+ years of experience developing ML/DL models for image analysis
  • Demonstrated experience with segmentation, detection, and/or quantitative imaging algorithms (2D and/or 3D)
  • Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Experience with modern architectures (U‑Net variants, detection frameworks, transformers, or foundational models)
  • Familiarity with DICOM and medical imaging workflows
  • Strong understanding of evaluation metrics (Dice, IoU, ROC/AUC, sensitivity/specificity)
  • Experience with version control and collaborative development (e.g., Git)
  • Demonstrated ability to produce clear, structured technical documentation
  • Experience using modern LLM‑based coding assistants (e.g., Claude, Codex, or similar) to enhance development workflows (Strongly preferred)
  • Experience developing and documenting AI solutions for clinical translation or regulatory submission (e.g., FDA 510(k))
  • Familiarity with Good Machine Learning Practice (GMLP)
  • Experience collaborating with regulatory, quality, or cybersecurity teams
  • Exposure to software security principles (e.g., secure coding, vulnerability assessment, penetration testing concepts)
  • Experience with large, multi‑institutional datasets
  • Familiarity with radiology workflows and quantitative imaging biomarkers
  • Experience with cloud or high‑performance computing environments
  • Experience deploying models into research or clinical environments
Academic and Career Development Opportunities

Significant opportunities for authorship on high‑impact manuscripts, active participation in multi‑institutional research collaborations, opportunities to contribute to grant proposals and funded research initiatives, exposure to translational AI development including projects targeting FDA 510(k) clearance, and the ability to build a strong academic portfolio in parallel with real‑world clinical impact.

Key Attributes
  • Highly collaborative and team‑oriented
  • Detail‑oriented with strong commitment to documentation, reproducibility, and auditability
  • Ability to operate effectively in a translational, regulatory‑aware environment
  • Strong interest in delivering clinically impactful AI solutions
Compensation

In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of the compensation range for this role. This is an estimate offered in good faith and a specific salary offer takes into account factors that are considered in making compensation decisions including but not limited to skill sets, experience and training, licensure and certifications, and other business and organizational needs. It is not typical for an individual to be hired at or near the top of the salary range and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current salary range is $104,000 – $186,160 per year for the role of Staff Software Engineer / Machine Learning Engineer – Radiology. Explore our exceptional benefits!

Equal Opportunity Employer

St. Jude is an Equal Opportunity Employer. No Search Firms. St. Jude Children's Research Hospital does not accept unsolicited assistance from search firms for employment opportunities. Please do not call or email. All resumes submitted by search firms to any employee or other representative at St. Jude via email, the internet or in any form and/or method without a valid written search agreement in place and approved by HR will result in no fee being paid in the event the candidate is hired by St. Jude.

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