- Work with and learn from a strong team of engineers hailing from MIT, CMU, Facebook, Stripe, and more. We grow our new grads through mentorship and ownership.
- Own projects end-to-end, from initial design, to prototype, to large-scale rollout.
- Design and implement backend models and API endpoints for complex scientific workflows.
- Design and implement rich frontend components and architecture. We build and operate one of the largest, richest React applications out there and power complex scientific analyses.
- Have a BS and/or MS degree in computer science, math or a related technical field.
- Build software with a product-first approach. You ship code quickly and care about the real world impact of your code.
- Have strong abilities in problem solving and iterating on feedback.
- Enjoy ownership and building key pieces of product.
- Are interested in learning more about life science (prior knowledge is not required; desire to learn is a must).
YOU MIGHT WORK ON
Check out our engineering blog for some examples of past work. Here are some other examples of recent and future projects:
- Tools to simulate DNA assembly - computers can assist scientists by informing scientific decisions with complex calculations. Our bulk assembly tool allows scientists to simulate constructing hundreds of DNA constructs in parallel, visualizing the end result and highlighting potential issues.
- Complex querying and visualization tools - The big advantage of keeping all your data stored in one system is that you can query that data all at once, without digging up multiple sources. This works best when you can query Benchling the same way you can query a database: by choosing from a large number of possible filters on any related objects, joining together various sources, and performing aggregations. We want to enable this in a user-friendly experience, without leaving your browser.
- A customizable computation platform - Biologics are typically large protein complexes composed of a number of shared parts. We want to let scientists specify computations that can aggregate data across all of these related entities to surface derived values, which can be used in everything from data analysis to naming schemes. Scientists may develop custom protein analysis algorithms or proprietary validation logic - they'll be able to hook in custom code and push that computation to Benchling.
- Data warehouse as a product - customers can click a button and spin up a fully-managed, query-able warehouse with all their Benchling data in one place. Customers get the full power of Redshift/Postgres without having to manage ETL pipelines or tune Postgres configuration values.
- A new permissions system - Our existing permission system relies on Read/Write/Admin access that can only be applied at the top level of the file hierarchy. To roll out to enterprises with 500+ users, we need to offer them fine-grained control around exactly what actions a user can take and let them manage these in a hierarchical fashion.