Senior Neuroengineering Developer
$105kThe Johns Hopkins University Applied Physics Laboratory
Description Do you enjoy designing software, algorithms, and cloud-based tools that accelerate neuroscience discovery? We are seeking a Senior Neuroengineering Developer to lead the design, development, and deployment of computational tools, software, and algorithms for large-scale structural and functional neuroscience datasets. You will help build the next generation of connectomics analysis capabilities while advancing our understanding of nervous system function. We are a large collaborative and multidisciplinary team of engineers and scientists engaged in groundbreaking research for the nation, spanning neuroengineering, artificial intelligence, robotics, and complex systems. As a Senior Neuroengineering Developer Your primary role will be leading the development and maintenance of computational pipelines, scalable analysis tools, and cloud-based environments for interrogating large-scale neuroscience datasets, including structural and functional connectomes collected across multiple species and imaging modalities. You will develop, maintain, and extend the software infrastructure that supports cloud-scale storage, visualization, and analysis of petabyte-scale neuroscience datasets used by researchers worldwide. You will architect and implement scalable software systems that support automated neuroscience data processing, visualization, and analysis on high-performance computing resources. You will communicate key findings both internally across teams and externally to sponsors, and will play a major role in research proposal development. You will participate in the neuroscience and APL community, and present data at Lab meetings and to department management. You will participate in collaboration and innovation opportunities to help ensure the success of APL's mission. Qualifications You meet our minimum qualifications for the job if you... Hold a Ph.D. in Computer Science, Computer Engineering, Data Science, Artificial Intelligence, Neuroscience, Biomedical Engineering, or related subject areas. Have 5+ years experience developing software or computational methods for large-scale structural and/or functional neuroscience datasets. Have demonstrated experience designing and deploying production software using modern cloud infrastructure (AWS or equivalent), containerization (Docker, Kubernetes), databases (SQL and/or NoSQL), RESTful APIs, and modern software engineering practices. Have experience with collaborative software engineering practices including Git, automated testing, CI/CD, and code review. Demonstrate excellent research and oral communication skills, and the ability to work with a wide range of collaborators. Are able to obtain Secret level security clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. You'll go above and beyond our minimum requirements if you... Have experience designing cloud-native data platforms for petabyte-scale scientific datasets. Have experience developing distributed data processing pipelines or microservices. Have experience building interactive visualization or web applications for scientific datasets. Have experience with computer vision, image processing, machine learning, or AI applied to biological imaging datasets About Us Why Work at APL? The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates. At APL, we celebrate our differences of perspectives and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL's campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law.APL is committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact View email address on click.appcast.io. The referenced pay range is based on JHU APL's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level with consideration for internal parity. For salaried employees scheduled to work less than 40 hours per week, annual salary will be prorated based on the number of hours worked. APL may offer bonuses or other forms of compensation per internal policy and/or contractual designation. Additional compensation may be provided in the form of a sign-on bonus, relocation benefits, locality allowance or discretionary payments for exceptional performance. APL provides eligible staff with a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development. Applications are accepted on a rolling basis. Minimum Rate $105,000 Annually Maximum Rate $290,000 Annually #J-18808-Ljbffr The Johns Hopkins University Applied Physics Laboratory
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