Entry Level Materials Science Engineering Jobs in Georgia

Machine Learning Research Engineer (Scientific & Engineering AI)
Urgent Hiring Requirement

Minimum Qualification: PhD in a relevant technical field.

This is an urgent requirement with an anticipated start date within 2 weeks. Priority will be given to candidates who can interview promptly and begin within two weeks of selection.

Job Summary

We are seeking a highly motivated Machine Learning Research Engineer (Scientific & Engineering AI) with strong expertise in Machine Learning, Deep Learning, Computer Vision, and AI research. This role is intended exclusively for PhD graduates or candidates near completion from reputable universities.

Candidates with a strong academic research background in Machine Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Computing, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or related fields are encouraged to apply.

Ideal candidates will combine strong ML/DL expertise with domain knowledge in mechanical engineering, materials science, manufacturing systems, physical systems, scientific computing, or simulation-driven engineering applications.

Research experience gained during a PhD program will be considered equivalent to professional industry experience.

This is an urgent hiring requirement, and we are actively seeking candidates who can start within the next 2 weeks.

Education Requirement
PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, Data Science, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or a related technical field.
Candidates currently pursuing a PhD with anticipated graduation within the next 3-6 months are also encouraged to apply.
Only PhD candidates will be considered for this role.
Candidates with only a Master's degree will not be considered.
Key Responsibilities
Design, develop, train, and optimize Machine Learning and Deep Learning models for real-world applications.
Own the complete ML lifecycle including data collection, annotation, preprocessing, model training, fine-tuning, evaluation, optimization, and deployment.
Develop and deploy advanced deep learning architectures including CNNs, LSTMs, ConvLSTMs, Graph Neural Networks (GNNs), Reinforcement Learning, and Transformer-based models.
Conduct experiments, evaluate model performance, and drive continuous algorithmic improvements.
Work with large-scale datasets for model training, validation, and testing.
Optimize and deploy AI models for scalable and efficient real-world applications.
Translate research concepts into scalable, production-ready AI systems.
Collaborate with cross-functional engineering and research teams to integrate ML models into real-world applications.
Document methodologies, experimental findings, and technical solutions.
Contribute to technical innovation initiatives and advanced AI research activities.

Required Qualifications
Strong PhD research background in Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Machine Learning, Computational Engineering, Applied Physics, Materials Informatics, or related areas.
Strong programming experience with Python and C++.
Hands-on experience with PyTorch, TensorFlow, Keras, Scikit-learn, or similar ML frameworks.
Strong understanding of Machine Learning, Deep Learning, Neural Networks, Computer Vision, and AI algorithms.
Experience developing and training advanced deep learning models and architectures.
Solid mathematical foundation in linear algebra, probability, statistics, optimization, and applied machine learning.
Experience working with Linux environments, Git, Docker, and modern development workflows.
Demonstrated research experience through publications, thesis work, academic research projects, or equivalent research contributions.
Strong ability to independently research, prototype, and deploy AI solutions.
Experience applying machine learning or deep learning techniques to engineering, manufacturing, materials science, physical systems, scientific computing, simulation, or industrial applications is highly desirable.

Preferred Qualifications
Publications in leading AI, Machine Learning, Computer Science, Scientific Computing, Computational Engineering, Materials Science, or Applied Physics conferences and journals.
Experience transitioning AI/ML models from research environments into production systems.
Experience with CUDA, GPU acceleration, distributed computing, high-performance computing (HPC), or parallel computing environments.
Experience handling large-scale, real-world datasets.
Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization techniques.
Experience working with data generated from CAD, CAE, CFD, FEA, multiphysics simulations, manufacturing processes, materials characterization, laboratory testing, or other engineering and scientific workflows.

Technical Skills
Python, C++
PyTorch, TensorFlow, Keras, Scikit-learn
Machine Learning and Deep Learning
Computer Vision
Reinforcement Learning
Graph Neural Networks (GNNs)
Transformer Architectures
Linux, Git, Docker
CUDA and GPU Computing
Scientific Computing and Optimization
Physics-Informed Machine Learning (Preferred)
Engineering and Scientific Data Analysis (Preferred)