Deep Genomics is hiring a

Senior/Lead Scientist - Computational Chemistry

Toronto, Canada
Full-Time
About Us

Deep Genomics aims to revolutionize drug development using artificial intelligence built to decode RNA biology and RNA therapeutics. Our proprietary AI platform enables us to decode the enormous complexity of RNA biology to find novel targets, mechanisms, and molecules that are not accessible through traditional methods. Founded in 2015, our multidisciplinary team includes expertise in a diverse range of disciplines including those found in a traditional drug company, as well as machine learning, statistical genetics, applied data science, deep bioinformatics, software engineering and laboratory automation. Deep Genomics is based in Toronto, Ontario and Cambridge, Massachusetts.

What You’ll Be Doing

We are seeking a world class computation chemist with experience modeling chemical entities relevant for non-traditional drug discovery.  The successful candidate will be positioned at the interface of our efforts in chemical innovation and predictive systems, and have the ability to impact both in a fundamental way.  You will play a key role in the biophysical understanding and development of  oligonucleotide drugs and the design of oligonucleotide building blocks.  You will be a leader in our computation chemistry efforts and responsible for building necessary computational infrastructure, as well as helping implement the long term visions that leverage structure based drug design in biomacromolecules.  As Deep Genomics is a highly matrixed company, you will be working alongside people of diverse expertise and backgrounds.  This will include regular interface with biology (in vitro & in vivo), pharmacology, clinical research, chemistry and computational divisions.  You should be a self starter, and excited about working in a high energy startup environment.

What you need:

  • PhD in computational chemistry, chemical informatics, physical organic chemistry, computational biology, biophysics, or related field with 2 years additional drug discovery experience as a computational chemists: OR
  • MS in computational chemistry, with 5 years addition drug discovery experience as a computational chemist: OR
  • BS in natural science with 8-10 years additional drug discovery experience as a computational chemist.
  • Experience with a variety of computational tools and methodology including; docking, virtual screening, molecular dynamics, free energy perturbation, homology modeling, quantum mechanics, pharmacophore elucidation
  • Experience modeling biomacromolecules (preferably oligonucleotides), and their building blocks, in pharmaceutically relevant backgrounds

What you bring:

  • Comprehensive experience in data mining and data analysisStrong scientific programming skills (e.g. Python, R) and pipelining tools (e.g. Knime)
  • Familiarity with standard computational chemistry packages (examples may include, Maestro/Schrodinger, MOE/Chemical Computing Group, and/or OpenEye tools)
  • Ability to tailor/modify/parameterize computational chemistry force fields for accurate prediction of novel chemical matter
  • Demonstrated ability to successfully thrive in a dynamic, fast-paced, innovative environment with an emphasis on teamwork, collaboration, and communication within team and across the department
  • Familiarity with ML/AI computational approaches is desirable but not required

What we offer:

  • A highly competitive salary and meaningful equity compensation (ESOPs)
  • A wide array of company-paid benefits
  • Remote work possible
  • Exceptional opportunities for learning and growth working alongside a world-class team of researchers and software developers working at the intersection of the most exciting areas of science and technology.

Deep Genomics thanks all applicants, however only those selected for an interview will be contacted.    

Deep Genomics welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.

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