System Engineer: Machine Learning
Machine Learning Engineer
BenevolentAI, founded in 2013, creates and applies AI technologies to transform the way medicines are discovered and developed. BenevolentAI seeks to improve patient’s lives by applying technology designed to generate better data decision making and in doing so lower drug development costs, decrease failure rates and increase the speed at which medicines are generated. The company has developed the Benevolent Platform™ - a discovery platform used by BenevolentAI scientists to find new ways to treat disease and personalise drugs to patients.
BenevolentAI is HQ’d in London with a research facility in Cambridge (UK) and further offices in New York and Antwerp. BenevolentAI has active R&D drug programmes from discovery to Phase II in disease areas such as ALS, Parkinson’s, Ulcerative Colitis and Sarcopenia.
The Engineering team is focused on applying engineering rigor and discipline to our growing software platform. We embrace modern microservice architecture and use Kubernetes to orchestrate a machine learning platform that’s tailored to empower drug discovery scientists. We are keen to hear from both entry level and experienced Machine Learning Engineers who will work with the full cycle of machine learning, from data ingestion, data modeling, and feature extraction, to modeling, evaluation, and deployment.
Drug discovery is a truly hard problem: by one count, there are north of 7000 diseases with no treatment. To meet this challenge, we work hand in hand with in-house biologists. We use a spectrum of techniques ranging from expert augmentation to deep learning. We integrate nuanced, complex and vast biomedical data into one of the largest biomedical graphs. Our team is made of talented and highly motivated bioinformaticians, engineers, AI scientists, drug discoverers, and product managers. Come help us build software that makes the world a better place, in a very tangible way.
- Develop our machine learning pipeline tailored to empower drug discovery scientists.
- Productize, and serve AI models built for drug discovery.
- Develop ETL pipeline and services to support model benchmarking and evaluation.
- Develop and operate systems with microservice architecture in our on-premise Kubernetes cluster.
- Deploy AI powered products in both internal and external disease programs.
We’re looking for someone with...
- Degree in Computer Science, Machine Learning, Artificial Intelligence (BSc/MSc/PhD) or related academic discipline.
- Experience in a product team, in a commercial environment, as a software engineer (full-time or internship) or relevant roles.
- Proven track records for building complex applications, dealing with complex data, and delivering user experiences with attention to code quality and testing.
- Expert of machine learning theory, specifically supervised learning, generative model, reinforcement learning, active learning, Bayesian methods, etc.
- A robust knowledge of some of these is needed:
- Language: Python, Java
- ML Framework: any of Tensorflow, PyTorch, MxNet
- Backend: flask, spring, SQL/NoSQL/Graph Databases, REST APIs, GraphQL
- Infrastructure and DevOps: Docker, Kubernetes, CI/CD
Who are we?
We have assembled a diverse, exceptionally talented and spirited team to tackle the most pressing and challenging problems at the intersection of artificial intelligence and drug discovery. We bring our ideas and passion for new technology and medicine discover to life by questioning traditional scientific dogmas.
Our core values reflect who we are and how we work and they are so important to achieve our mission: Bring better medicine to patients faster.
Put patients first. Drive to delivery. Break boundaries. Own the solution.
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