Do you want to be a part of something innovative and cutting edge? If your answer is yes, then join our team of more than a hundred software engineers, designers, project managers and software specialists that are smart, creative and excited by what they do!
Some of our ground-breaking work includes: advanced computer-adaptive algorithms (only one that's peer-approved in the country); mobile support for the user interfaces; learning management systems with social media features; user interfaces that are universally accessible to people with or without disabilities; innovative, machine-scorable items and that's just to name a few. The American Institutes for Research (AIR) is a leading professional services firm with a growing software engineering and product development team. The Assessment group provides assessment services and technical assistance to school systems nationwide. We design and build things that are inspiring and make a real impact in the online testing industry and we are currently seeking a Senior Software Engineer, Machine Learning for their office in Washington, D.C.
The machine learning engineer work will be an integral part of the machine learning / scoring development team within AIR. This diverse group of professionals that include mathematicians, computer scientists, psychometricians, statisticians and software engineers, provide custom machine learning solutions for our clients as well as internal support systems.
The right candidate will have the skills needed to perform full life-cycle software development to take research ideas and initiatives from concept/prototype to production quality software. This includes participation in research discussions, requirements gathering, application and database design, system documentation, writing and unit-testing efficient code, and deployment. Implement high performance, scalable and reliable software solutions in Python on Linux or Windows platforms Develop and deploy synchronous and asynchronous REST API web services using Python frameworks Develop effective methods of ML model testing during all stages: development, deployment, and recalibration Train machine learning models, analyze performance metrics, and communicate results with visual and statistical aids Analyze, visualize, and summarize large multidimensional datasets Utilize best practices for software development of high performance systems around design, coding, automated unit and regression testing and deployment
American Institutes For Research