AI/ML Engineer - Sequence Learning, Open to flexible working

Location
Brentford (City/Town), London (Greater)
Salary
£Competitive
Posted
23 Nov 2021
Closes
14 Dec 2021
Ref
314362
Role
IT
Contract Type
Permanent

At GSK we see a world in which advanced applications of machine learning and AI will allow us to develop novel therapies to existing diseases and to quickly respond to emerging or changing diseases with personalized drugs, driving better outcomes at reduced cost with fewer side effects. It is an ambitious vision that will require the development of products and solutions at the cutting edge of machine learning and AI. If that excites you, we'd love to chat.

The AIML Sequence Learning Team applies machine learning and AI methods to DNA and RNA sequence data from large-scale human genetic, functional genomic and single cell experiments. DNA sequence variations are linked to disease via changes in the nature and abundance of protein and RNA gene products. The Sequence Learning group builds models directly from DNA/RNA sequence in addition to other data sources such as chromatin state. Models that operate on a sequence level can infer changes in protein/RNA states and the associated cellular function from sequence alone. This is an exciting methodological advance at the intersection of NLP inspired language models applied to biological data.

We're looking for a highly accomplished AI/ML Engineer to join our team. Competitive candidates will have a track record of shipping high performing AI/ML powered products in production environments. They are highly accomplished Machine Learning Engineers, with breadth across Machine Learning Methods, as well as depth in at least one area. Additionally, they are accomplished software engineers with a track record of shipping stable, tested, performant code and services in an agile environment.

Strong candidates will have graduate studies in Computer Science, Applied Mathematics, or the equivalent, and a passion for solving challenging problems in Artificial Intelligence and Machine Learning. Educational or professional background in the biological sciences is a plus; passion to help therapies for new and existing diseases, and a pattern of continuous learning and development is mandatory.

The AI/ML team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career and development from day one.

Basic Qualifications:

We are looking for professionals with these required skills to achieve our goals:

  • Graduate studies in Computer Science or Applied Math, undergraduate studies in Computer Science and relevant graduate studies in the life sciences with a focus on AI/ML techniques, or undergraduate studies in Computer Science and equivalent work history. Candidates with graduate studies in CS and biological sciences or equivalent work history will be highly competitive.
  • Experience as a software engineer with intermediate skills in python or C++
  • Experience with PyTorch, Tensorflow, or other deep learning frameworks
  • Experience across the ML stack

Preferred Qualifications:

If you have the following characteristics, it would be a plus:

  • PhD in computer science
  • Knowledge in disease biology, molecular biology and biochemistry
  • Experience with biological data (e.g., genomics, transcriptomics, epigenomics, proteomics)
  • Understanding and application of best practices in Machine Learning
  • Track record of contributing to open source projects

Why GSK?

Our values and expectations are at the heart of everything we do and form an important part of our culture.

These include Patient focus, Transparency, Respect, Integrity along with Courage, Accountability, Development, and Teamwork. As GSK focuses on our values and expectations and a culture of innovation, performance, and trust, the successful candidate will demonstrate the following capabilities:

  • Operating at pace and agile decision-making – using evidence and applying judgement to balance pace, rigour and risk.
  • Committed to delivering high quality results, overcoming challenges, focusing on what matters, execution.
  • Continuously looking for opportunities to learn, build skills and share learning.
  • Sustaining energy and well-being
  • Building strong relationships and collaboration, honest and open conversations.
  • Budgeting and cost-consciousness

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