R&D Data Analysis & Machine Learning Engineer

University of Texas; Austin (TX); On-site; USD 104,000 - 174,000; Full time

Job summary

Applied Research Laboratories at UT Austin seeks an R&D Data Analysis and Machine Learning Software Engineer to advance data modeling, algorithm development, and software tooling for environmental science research.

The role blends research with software engineering: designing and testing algorithms, implementing in MATLAB and Python on UNIX/Linux, documenting results, and presenting findings to sponsors and internal groups.

Qualifications

• Bachelor’s degree in natural or engineering sciences including computer science/electrical engineering or related discipline.
• Three or more years of experience with data algorithms, ML methods, and applied research.
• Experience developing applications in MATLAB and Python in UNIX/Linux environments.

Responsibilities

• Design, develop, configure, apply, test, and support data analysis and machine learning algorithms.
• Design and write flexible software per designs and test to meet project requirements.
• Prepare technical documentation and present results at internal/external meetings.
• Communicate with team members, supervisors, and sponsors for timely implementation.
• Review peer software to improve designs and deployments.

Skills

Data analysis

Machine learning

Python

MATLAB

UNIX/Linux

C/C++

SQL

TensorFlow

Keras

Experimentation

Education

Bachelor's degree in natural or engineering sciences including computer science/electrical engineering

Master’s or Ph.D. in related discipline

Tools

MATLAB

Python

SQL

Git

Job description

Applied Research Laboratories at UT Austin seeks an R&D Data Analysis and Machine Learning Software Engineer to advance data modeling, algorithm development, and software tooling for environmental science research.

The role blends research with software engineering: designing and testing algorithms, implementing in MATLAB and Python on UNIX/Linux, documenting results, and presenting findings to sponsors and internal groups.