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Machine Learning Engineer Jobs in Chattanooga, TN

Bespoke Labs

Machine Learning Engineer

Chattanooga, TN · 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 ...

Varsity Tutors

Machine Learning Tutor

Chattanooga, 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 ...

EPB

Quantum Software Engineer Fellow

Chattanooga, TN · On-site

Machine learning * Python programming language CERTIFICATIONS& OTHER REQUIREMENTS * US citizenship is required * Excellent written and oral communicationskills and interpersonal skills * A motivated ...

EPB

Quantum Software Engineer Fellow

Chattanooga, TN · On-site

Machine learning * Python programming language CERTIFICATIONS & OTHER REQUIREMENTS * US citizenship is required. * Excellent written and oral communication skills and interpersonal skills. * A ...

Varsity Tutors

Data Science Tutor

Chattanooga, TN · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Chattanooga, TN

Machine Learning Engineer information

See Chattanooga, TN salary details

$28.7K

$117.2K

$176.2K

How much do machine learning engineer jobs pay per year?

As of Aug 4, 2026, the average yearly pay for machine learning engineer in Chattanooga, TN is $117,248.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,400.00 and $141,100.00 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 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 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 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.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Chattanooga, TN? The most popular types of Machine Learning Engineer jobs in Chattanooga, TN are:
What are popular job titles related to Machine Learning Engineer jobs in Chattanooga, TN? For Machine Learning Engineer jobs in Chattanooga, TN, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Chattanooga, TN look for? The top searched job categories for Machine Learning Engineer jobs in Chattanooga, TN are:
What cities near Chattanooga, TN are hiring for Machine Learning Engineer jobs? Cities near Chattanooga, TN with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Chattanooga, TN as of July 2026, with employment types broken down into 87% Full Time, 10% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $117,248 per year, or $56.4 per hour.

Machine Learning Engineer

Bespoke Labs

Chattanooga, TN

Full-time

Re-posted 17 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

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 preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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