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GSK in Germany is seeking a Staff AI/ML Engineer – Controllable Biology to drive state-of-the-art modeling of biological networks and sequences from genomic and single-cell data. You will translate biological challenges into machine learning problems and deliver end-to-end AI/ML powered solutions in an agile environment.
You have a Master's in a related field and 5+ years in deep learning and software development, with Python and PyTorch.
At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade. Our R&D focuses on discovering and delivering vaccines and medicines, combining our understanding of the immune system with cutting-edge technology to transform people's lives. GSK fosters a culture ambitious for patients, accountable for impact, and committed to doing the right thing, making sure that we focus our efforts on accelerating significant assets that meet patients' needs and have the highest probability of success. We're uniting science, technology, and talent to get ahead of disease together.
Our approach to R&D
The AI/ML Controllable Biology Team applies machine learning and AI methods to biological network s and sequence data from large-scale human genetic, functional genomic and single - cell experiments. Models of control of biological net works have the potential to be transformative in drug discovery, empowering us to find new life - saving medicines.
We are looking for a Staff AI/ M L Engineer - Controllable Biology . Competitive candidates will have a track record in developing SOTA deep learning models for solving challenging real world scientific problems. You should be an outstanding scientist with in-depth knowledge in modern machine learning. You can convert vaguely described biological/drug discovery challenges into well-defined machine learning problem s . You can independently execute and deliver full AI/ML driven solution from sourcing training data, design and implementing SOTA machine learning models, testing, benchmark ing and product driven research for model performance improvement, to shipping stable, tested, performant code and services in an agile environment.