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Accelerate research in the natural and physical sciences with a group of research scientists and engineers on a mission-driven team. Together, you will apply ML and other computational techniques to a wide range of challenging problems.
The role
As a Software Engineer in Science, you will collaborate with researchers and engineers to develop and run experiments exploring new applications of AI in various scientific domains. The team is groundbreaking in many different areas, from the foundational to the practical, and you may take part in exploratory work, work in a maturing area, or general infrastructure.
In this role you will:
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Use your domain knowledge in the sciences to design, develop and implement high-performance simulations, tools, and analyses to challenge root cause scientific theories.
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Apply your software engineering expertise to produce high quality, reusable code and components to tackle meaningful problems in the natural sciences.
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Share ideas with other specialists in the team and be highly collaborative, striving to cultivate a culture of development and advancement.
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Employ groundbreaking technology and techniques to contribute to solving some of the hardest problems.
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Incorporate your passion for science and any previous knowledge or academic experience to join a close-knit team of hardworking individuals who openly collaborate with one another.
About you
Software Engineers come from diverse backgrounds, sometimes with degrees in Computer Science and sometimes with experience with real problems, or both. We are most interested in people with the following qualifications:
Minimum qualifications:
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A degree in the natural or physical sciences with a minor in Computer Science or extensive applied experience -OR- a degree in Computer Science with research or applied experience in the natural or physical sciences.
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Software engineers with experience in software design and development.
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Experience with concurrent and distributed software algorithms and architectures.
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Proven experience in Python, C++, and interoperability between the two.
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Preferred qualifications:
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Scientific knowledge (particularly biology, chemistry, mathematics)
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Experience working in a research environment.
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Knowledge of accelerator optimization.