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Scientific Machine Learning Jobs in Atlanta, GA (NOW HIRING)

SOLTECH

Data Scientist

Duluth, GA · On-site

Senior Data Scientist (Machine Learning & MLOps) Our client is seeking a Data Scientist (Machine Learning & MLOps) to help build the next generation of its intelligent water utility platform. This is ...

Coca-Cola

Machine Learning Architect II

Atlanta, GA

Partner with data science teams to transition machine learning models from experimentation to production environments, packaging models into robust Docker containers for scalable and reproducible ...

Speria

Machine Learning Operations Engineer

Atlanta, GA · On-site

$120 - $180/hr

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. * 2-4 ... Experience working with machine learning lifecycle tools and platforms (e.g., MLflow or similar)

United Airlines

Data Scientist

Atlanta, GA

Design, develop, train, and optimize machine learning and deep learning models for marketing ... Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics ...

Omnissa

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$162K - $342K/yr

What will you bring toOmnissa? * 5+ years of experience in machine learning engineering or data science roles. * Strongproficiencyin Python and ML frameworks (e.g.,PyTorch, TensorFlow,Scikitlearn)

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Scientific Machine Learning information

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How much do scientific machine learning jobs pay per hour?

As of Aug 15, 2026, the average hourly pay for scientific machine learning in Atlanta, GA is $30.27, according to ZipRecruiter salary data. Most workers in this role earn between $18.51 and $38.61 per hour, depending on experience, location, and employer.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

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

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

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

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Atlanta, GA?

For Scientific Machine Learning jobs in Atlanta, GA, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Atlanta, GA look for?

The top searched job categories for Scientific Machine Learning jobs in Atlanta, GA are:

What cities near Atlanta, GA are hiring for Scientific Machine Learning jobs?

Cities near Atlanta, GA with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Atlanta, GA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 21% Part Time, 1% Temporary, and 4% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $62,963 per year, or $30.3 per hour.

Next Insurance - Machine Learning Engineer

Beyond SOF

Atlanta, GA • On-site

Full-time

Re-posted 5 days ago

Job description

Machine Learning Engineer
Experience level: Mid-senior
Experience required: 4 Years
Education level: Bachelor's degree
Job function: Information Technology
Industry: Financial Services
Total position: 1
Relocation assistance: Limited assistance
Visa : Only US citizens and Greencard holders
Job Summary
We are looking for a driven Machine Learning Engineer to help us bring software development rigor to the ML life-cycle within the business insurance industry. As an experienced innovator partnering with the marketing and growth teams, this is a massive opportunity to drive high growth impact at a hyper-growth startup. Your job is to be a full stack ML engineer, supercharging all aspects of scaling Machine Learning at NEXT: application design and architecture, scalable deployment of inference solutions as APIs, data enrichment as a service, and model monitoring.
You will be joining an innovative division of NEXT based in Atlanta: Data Labs. The mandate of Data Labs is to build software and data solutions that meaningfully impact marketing, funnel, risk, and servicing/claims experiences.
What You'll Do:
  • Empower our team of data scientists to rapidly develop and deploy ML solutions.
  • Leverage software engineering best-practices to create and deploy data-intensive and machine learning inference products.
  • Understand the data and dig deep to extract actionable insights.
  • Think creatively and outside the box to answer desired experimental questions as well as exposing opportunities to create business value.
  • Work cross-functionally with marketing, engineering, product, senior management, and external partners.

What We Need:
  • 3+ years of hands-on experience in the complete software development life-cycle with demonstrated professional experience with machine learning
  • Strong command of Python and the standard web development frameworks (e.g. Django, Flask, FastAPI) & fluency with database technologies, SQL, and Python data packages
  • Demonstrated experience deploying models and applications to a cloud environment using tools like Docker and Kubernetes.
  • Demonstrated experience with software engineering best-practices, such as Test-Driven Development and continuous integration/deployment pipelining
  • Experience with ML-oriented data pipelining tools (dbt, Airflow, Prefect, DVC, etc.)

Unstoppable Qualities:
  • Experience in the insurance industry (strong fintech/lending experience will also be considered)
  • Experience with GitLab

Apply