What's the role and how do you fit in?
BenevolentAI harnesses artificial intelligence to accelerate scientific discovery by making sense of highly fragmented information to develop new medicines for hard to treat diseases, using AI as a force for good. Valued at $2bn in 2018, we are the largest independent AI company in the world.
Our software engineers work in multi-functional squads working alongside the best scientists, machine learning gurus, world-renown biologists, and extraordinary scientists to make the world a better place.
Our tech stack includes Java, Python, variety of graph databases, ElasticSearch, Linux, Ansible, Kubernetes, Mesos, Puppet, Chef, Docker, GitLab, Bamboo, Travis, Terraform, AWS, EC2, Jenkins, Sonar, Git, VMWare, Saltstack, Atlassian, MySQL, NoSQL, Hadoop, Spark, Nginx, Apache, Cassandra and more.
We don't stop there; we empower our engineers to look at improving our engineering stack wherever possible. You will be encouraged to bring in the newest advancements and cutting-edge solutions, and to collaborate on the ever evolving needs of our technology.
What will you be accountable for?
The role also calls for microservice-style web services, database servers, graph data structures, all of which are deployed onto our Kubernetes cluster.
What skills, experience and qualifications do you need to be successful in this role?
We do not offer gym subsidies, kiddi vouchers, Monday breakfasts, nor transportation assistance in our NY office - YET! We are continually working hard on providing you with the best environment, just give us some time to catch up.
What are the Squads?
Depending on your experience and what you are passionate about, you can expect to work across the following:
Our data pipelines are primarily Java-based, with documents making their way through Cassandra, Spark, before arriving into graph databases and Elastic Search. We also ingest significant amounts of structured data on chemistry and biology from structured databases, such as ChEMBL.
There’s some significant complexity in our data-processing pipeline, so we’re interested in measuring, monitoring, acceptance testing and performance tuning.
We’re enthusiastic adopters of GraphQL which ties all our data and APIs together.
Machine Learning Squads
We primarily write in Python, using libraries such as TensorFlow, PyTorch and Keras for machine learning. We implement cutting-edge research from recent papers with these libraries, as well as producing our own novel research through relationship extraction (RE), named entity recognition (NER) and data pipelining.
NER and RE, you can also say Information Extraction (IE) turns documents from external sources (articles, patents, abstracts etc.) into new edges in our unstructured graph that represent a relation between two entities. Connecting the dots and identifying relationships powers our AI tools.
In particular we apply good engineering practices to the data-preprocessing stages of our machine learning stages, using PySpark for data processing, and systems such as dvc for storing versioned data and models.
We have built various interesting applications for our chemical and biological users which include:
Sometimes we need to build production-quality frontends, other times we quickly prototype interesting ideas to develop. We build unified API to access our Knowledge Graph and other services.
We make the most of modern frontend tech, including Angular6, Typescript/ES6, RxJS, Flux Pattern, SASS and Material Design. We have in-house UX and Design to make our applications useful and breathtaking.
We deploy everything to Kubernetes and RiseML. We manage our own clusters, as well as spinning up additional compute capacity on AWS.
Our in-house-cluster is also home to our Nvidia DGX-1 Deep Learning Supercomputer, which we use to analyse billions of currently theoretical molecules.
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