Director of Computational Chemistry

Salt Lake City, Utah
Highly competitive salary with industry leading benefits package in new state of the art HQ!
Nov 13, 2018
Dec 13, 2018
Work Function
Job Type
Full time

At Recursion, we combine experimental biology, automation and artificial intelligence to quickly and efficiently identify treatments for human diseases. The Director, Computational Chemistry is responsible for the development and use of computational chemistry methods for accelerating our current drug discovery programs and augmenting the AI platform leading to the discovery of high value medicines.



You’ll work to collaborate with multidisciplinary groups to oversee the development and onboarding of ligand-based drug design techniques and capabilities to complement and augment the image-based phenotypic drug discovery methods already employed at Recursion.

In this newly created position, you will report to the VP of Data Science while also working closely with our VP of Chemistry and will play a crucial role in the expansion of Recursion’s computational chemistry capabilities.


In this role you will:

  • Develop - You are both a chemist and technologist at heart and won’t be happy unless you are also writing code to be used throughout the organization.
  • Innovate - Just because something has been done consistently for decades doesn’t mean it’s optimal. You will innovate and bring new methods to bear while staying pragmatic about the value that exists from many tried and true approaches.
  • Question - You will be expected to share ideas and challenge the status quo in how to do drug discovery using the best of existing and new technology while striving to remove biases that come with traditional science.
  • Lead - You will lead a new team of computational chemists to both develop and use new methods for advancing Recursion’s platform.



  • Ph.D. in Computational Chemistry, Physical Organic Chemistry, Computer Science or Machine Learning
  • 3+ years experience in developing ligand-based drug design methods, including pharmacophore modeling, QSAR, 3D-QSAR, and conformational analysis
  • Experience developing new methods, tools and products in computational chemistry for drug design and virtual library assembly/screening
  • Experience with machine learning methods, including classification and regression techniques, pairwise comparison, multiple-instance learning, neural networks, deep learning and graph convolutions
  • Experience with Unix/Linux, Python and ML packages such as Scikit-Learn, TensorFlow, PyTorch, and/or Keras



  • Coverage of health, vision, and dental insurance premiums (in most cases 100%)
  • 401(k) with generous matching (immediate vesting)
  • Stock option grants
  • Two one-week paid company closures (summer and winter) in addition to flexible, generous vacation/sick leave
  • Commuter benefit and vehicle parking to ease your commute
  • Complimentary chef-prepared lunches and well-stocked snack bars
  • Generous paid parental leave (including adoptive)
  • Fully-paid gym membership to Metro Fitness, located just feet away from our new headquarters
  • Gleaming new 100,000 square foot headquarters complete with a 70-foot climbing wall, showers, lockers, and bike parking



We start with various human cell types in our automated laboratory, where we perturb biology over 100k different ways each week (e.g. model a disease of interest, add a chemical compound, or some combination of those). We image the resulting cells using high throughput microscopy, resulting in about 2 million new images or greater than 20 terabytes of data each week with well over a petabyte of imaging data accumulated to date. These data are analyzed using computer vision and machine learning methods that help us measure how cell morphology changes in subtle ways in each biological context. We can answer direct questions (e.g. are there compounds that rescues a disease-specific set of morphological changes), and over time as our dataset has increased, we can ask more complex questions across biology. From the outset, our approach is agnostic to preconceived notions and dogma in biology, enabling Recursion to operate in novel target space. Our data scientists and machine learning researchers work on some of the most challenging and interesting problems in computational drug discovery and collaborate closely with our biologists and chemists, along with some of the brightest minds in the deep learning community (Yoshua Bengio is one of our advisors), who help our team develop novel ways of tackling these problems.


Recursion is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. Recursion strictly prohibits and does not tolerate discrimination against applicants because of race, color, religion, creed, national origin or ancestry, ethnicity, sex, pregnancy, gender (including gender nonconformity and status as a transgender individual), age, physical or mental disability, citizenship, past, current, or prospective service in the uniformed services, or any other characteristic protected under applicable federal, state, or local law.