Data & Artificial Intelligence Research Engineer

United StatesRemoteFull-timemid

OtherArtificial Intelligence

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps.

Technical Tools

OtherArtificial Intelligence

This interdisciplinary engineering role sits at the intersection of materials science, machine learning, and software engineering.
You will research, develop, and maintain materials-aware AI capabilities that power sophisticated scientific and industrial applications.
The role combines hands-on production engineering with applied research, from translating mathematical concepts into code to evaluating model performance.
You will contribute to machine learning systems spanning interpretability, inverse design, uncertainty quantification, and scientific problem-solving.
Working closely with engineering, product, and research teams, you will help transform emerging AI ideas into scalable, customer-ready capabilities.
The position offers a highly autonomous, remote environment where technical rigor, collaboration, experimentation, and continuous learning are strongly valued.
Your work will directly support the development of more sustainable, high-performing materials and accelerate innovation across the physical sciences.

Write, develop, test, and maintain production-quality Python machine learning code and core libraries used to solve complex materials science problems.
Research, prototype, and develop new materials-aware machine learning models, algorithms, and product features.
Design and implement high-performance, scalable machine learning systems capable of supporting industrial-scale scientific applications.
Evaluate, test, and analyze the performance, impact, and reliability of machine learning models and continuously improve their effectiveness.
Contribute to advanced AI initiatives involving model interpretability, inverse design, uncertainty quantification, and other computational approaches to materials science.
Collaborate closely with product, engineering, and external research teams to translate scientific concepts and customer needs into practical AI capabilities.
Work across the full feature lifecycle, from concept development and technical design through implementation, testing, delivery, and ongoing maintenance.
Participate actively in code reviews, technical discussions, and engineering best-practice initiatives to maintain high standards of software quality.
Mentor other developers and contribute to a culture of knowledge sharing, technical growth, and continuous improvement.
Support research activities and contribute to technical publications and papers where appropriate, helping advance applied AI and machine learning in the physical sciences.

Requirements

~2 min read

8+ years of relevant professional experience, or 3+ years of professional experience with a master's or PhD in a quantitative discipline.
Strong proficiency in a programming language such as Python or Scala, with demonstrated ability to develop tested, production-quality software.
Proven experience implementing AI or machine learning solutions for customers and delivering measurable, quantifiable results.
Experience applying computational techniques to solve scientific or technically complex problems.
Deep understanding of core machine learning algorithms and their design, including approaches such as random forests and neural networks.
Strong knowledge of machine learning development practices, model evaluation, testing, performance analysis, and scalable system design.
Ability to communicate complex technical concepts, mathematical approaches, and design decisions clearly to both technical and non-technical audiences.
Strong collaboration skills and the ability to work effectively across engineering, product, research, and other multidisciplinary teams.
Demonstrated ability to operate autonomously, take ownership of technical outcomes, and continuously learn and improve.
Legally eligible to work in the United States.
Experience or academic background in Materials Science is preferred, along with extensive knowledge of statistics.
Experience working with large language models is preferred, with additional value placed on pre-training or fine-tuning foundation models.
Familiarity with SQL and relational databases is a plus, as is experience integrating applications with AWS services such as S3, RDS, and SQS.
Published research establishing expertise in AI/ML, natural language processing, computer vision, or a related technical domain is advantageous.