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Machine Learning Engineer Jobs in Tennessee (NOW HIRING)

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 ...

Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...

Showing results 41-60

Machine Learning Engineer information

See Tennessee salary details

$28.6K

$116.9K

$175.6K

How much do machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for machine learning engineer in Tennessee is $116,873.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,100.00 and $140,700.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Tennessee?

The most popular types of Machine Learning Engineer jobs in Tennessee are:

What cities in Tennessee are hiring for Machine Learning Engineer jobs?

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

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

For Machine Learning Engineer jobs in TN, the most frequently searched job titles are:

Infographic showing various Machine Learning Engineer job openings in Tennessee as of August 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 100% In-person job distribution, with an average salary of $116,873 per year, or $56.2 per hour.

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 13 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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