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Lead Research Engineer

About Us

Our interdisciplinary Science team applies the best of our artificial intelligence research to challenges within science. 

Snapshot

To succeed in this role, you will need to be passionate about advancing science using machine learning and other computational techniques. You'll join an interdisciplinary team of domain specialists, ML researchers and engineers exploring a diverse set of important scientific problems in biology, physics, mathematics, and other areas. Our work is organized into several longer-term focus areas which aim to achieve step changes to the state-of-the-art. You'll use our unique mix of expertise, data, and computational resources to experiment and iterate both rapidly and at scale.

The role

As an embedded Research Engineer you will collaborate with researchers and software engineers to develop and run experiments exploring new applications of AI to science problems. The team is pioneering in many different domains so you may take part in exploratory work validating early ideas or work in a maturing area to deepen and exploit a promising line of research. You will collaborate with internal and external researchers on cutting-edge science bridging AI and science. 
In this role you will:

  • Plan and execute rapid prototyping of machine learning techniques applied to problems in science.

  • Undertake exploratory analysis to inform experimentation and research directions.

  • Make improvements to the features, model architectures and training procedures of machine learning models

  • Implement tools, libraries, and frameworks to speed up research.

  • Report and present software developments, experimental results, and data analysis clearly and efficiently.

  • Collaborate with internal and external scientific domain specialists.

  • Contribute to the scientific knowledge and experience of the team with your own scientific domain knowledge.

  • The role will suit candidates who enjoy working in a heavily experimental setting with large and noisy datasets and who wish to immerse themselves in some of the most cutting-edge science, ML and AI research.
     

About you

Minimum qualifications:

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  • Master’s degree in computer science, electrical engineering, science, mathematics, or equivalent experience.

  • Applied experience with machine learning.

  • Experience with at least one programming language (with a preference for those commonly used in machine learning or scientific computing such as Python and C++).

  • Knowledge of linear algebra, calculus, and statistics equivalent to at least first-year university coursework.

  • Experience exploring, analyzing and visualizing data.

Preferred qualifications:

  • Experience working with large and noisy datasets.

  • Experience collaborating across disciplines.

  • Experience using TensorFlow, Jax, NumPy, Pandas or similar ML/scientific libraries.

  • Scientific knowledge (particularly biology, chemistry, and physics).

When assessing technical background, we will take a holistic view of the mix of scientific, ML and computational experience. We do not expect successful candidates to be experts in all fields simultaneously. However, since the Research Engineer role serves as a bridge between all three some experience in each is necessary. Candidates with particularly strong programming experience and less machine learning experience are encouraged to consider our Software Engineering role in the Science team.

 

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