Data & Artificial Intelligence Research Engineer

New

JobgetherMaterials science AI

Remote work within the United States.Full-TimeSenior

Salary$182,000–$210,000 USD

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Job Details

Experience: 8+ years of relevant professional experience, or 3+ years of professional experience with a master's or PhD in a quantitative discipline.
Required Skills: AWSPythonSQLMachine LearningScalaLLM

Have 8+ years of relevant professional experience, or 3+ years of professional experience with a master's or PhD in a quantitative discipline.
Demonstrate strong proficiency in a programming language such as Python or Scala.
Have experience developing tested, production-quality software.
Have experience implementing AI or machine learning solutions for customers and delivering measurable results.
Have experience applying computational techniques to scientific or technically complex problems.
Understand core machine learning algorithms and their design, including random forests and neural networks.
Have knowledge of machine learning development practices, model evaluation, testing, performance analysis, and scalable system design.
Be able to communicate technical concepts, mathematical approaches, and design decisions to technical and non-technical audiences.
Work effectively across engineering, product, research, and multidisciplinary teams; operate autonomously and take ownership of technical outcomes.
Materials science experience or academic background and extensive statistics knowledge are preferred.
Experience with large language models is preferred; pre-training or fine-tuning foundation models is an additional advantage.
SQL, relational databases, AWS services such as S3, RDS, and SQS, and relevant published research are advantageous.

Write, develop, test, and maintain production-quality Python machine learning code and core libraries for materials science problems.
Research, prototype, and develop materials-aware machine learning models, algorithms, and product features.
Design and implement high-performance, scalable machine learning systems for industrial-scale scientific applications.
Evaluate and analyze machine learning model performance, impact, and reliability, and improve model effectiveness.
Contribute to AI initiatives involving interpretability, inverse design, uncertainty quantification, and computational approaches to materials science.
Collaborate with product, engineering, and external research teams to translate scientific concepts and customer needs into AI capabilities.
Manage features across concept development, technical design, implementation, testing, delivery, and maintenance.
Participate in code reviews, technical discussions, and engineering best-practice initiatives.
Mentor developers and contribute to knowledge sharing and technical improvement.
Support research activities and contribute to technical publications and papers where appropriate.

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