Machine Learning Engineer (Materials)

<div class="content-intro"><p><strong>About CFD Research:&nbsp;</strong>Since its inception in 1987, CFD Research has delivered innovative technology solutions within the Aerospace &amp; Defense, Biomedical &amp; Life Sciences, Intelligence &amp; Sensing, and Energy &amp; Materials industries. CFD Research has earned multiple national awards for successful application and commercialization of innovative component/system technology prototypes, multi-physics simulation software, multi-disciplinary analyses, and expert support services. Based in Huntsville, Alabama where laboratory facilities and headquarters are located, CFD Research also has office and laboratory facilities in Dayton, Ohio, prototyping test and evaluation facilities in Hollywood, Alabama, and office facilities in Fort Walton Beach, Florida. CFD Research is an ISO9001:AS9100D registered company and is appraised at CMMI Level II for Services.&nbsp;<strong>CFD Research is a 100% ESOP (employee-owned company) recognized in Inc. Magazine's Inc5000 as a top growing company for four of the last five years. Learn more at&nbsp;<a href="https://www.cfd-research.com">www.cfd-research.com</a></strong></p></div><p></p> <p><strong>Machine Learning Engineer (Materials) (Huntsville, AL - OR - Dayton, OH)</strong></p> <p>The engineer in this role will join a multidisciplinary R&amp;D team focused on the development of machine learning algorithms focused on applications including, but not limited to, materials science, analytical chemistry, and microelectronics. We are looking for an engineer with core competencies in the areas of data-driven and quantitative modeling, as well as having the necessary science and engineering background to develop and deliver effective machine learning-based software solutions for our customers. This is a materials/chemistry focused role. The ideal candidate would have a background in both machine learning, and materials science and/or chemistry, physics, etc. The successful candidate will actively participate in all stages of the model life cycle, from ideation to deployment, for tasks that include, but are not limited to, exploratory data analysis, dataset preparation, model implementation, model training, model evaluation, model-based optimization, synthetic data generation, and first-principles modeling and simulation. They will collaborate with other engineers, subject matter experts, and program managers to achieve deployment of the algorithms to edge devices, desktop applications, and cloud services.</p> <p><strong>Basic Qualifications:</strong></p> <ul> <li>A background in materials science, chemistry, physics, etc.</li> <li>Strong programming skills in Python</li> <li>Experience with machine learning frameworks such as PyTorch or TensorFl