Research Scientist, Biophysics

SandboxAQ’s AI Simulation team develops new drugs and materials using a spectrum of AI and physics-based computational solutions. We are seeking an experienced and innovative Bioinformatics / Knowledge Graphs Researcher to amplify our ability to reason causally about biological systems, based on multimodal inputs from a variety of inputs, including simulation. The successful candidate will show strong ability in computational biology, including knowledge of cutting-edge machine learning techniques, particularly involving large graph data structures such as knowledge graphs. They will have experience creating maintainable software (Python) to those ends. These skills will be leveraged within a seasoned, agile, and multi-disciplinary group, including drug hunters with an excellent track record in drug discovery, computational chemists, physicists, AI experts, and software engineers.

Key responsibilities:

• Create and implement simulation methods to augment knowledge graphs
• Contribute to ongoing research towards applications of the above, including for biomarker ID and toxicity prediction.
• Present and communicate your ideas to an interdisciplinary team, including clients, other scientists, and non-technical team members.
• Invent new methods for patents and white journal articles and whitepapers. Present your work and ideas at conferences and in pitch meetings.
• Work to improve human health on a social scale. Help make better drugs and diagnostics, and make the tools to do so.

Basic qualifications:

• PhD in a relevant field (biophysics, computational chemistry or similar).
• At least 4+ years experience developing and applying biophysical simulation techniques to generate actionable insights
• 2+ years professional experience in software engineering or scientific software development
• Strong Python skills to develop simulation methods or add features to existing software both independently and with a team (version control, unit tests, code review)
• Track record of innovation in simulation methods and physics based property prediction

Preferred qualifications:

• Interest in and knowledge of AI-based molecular methods
• Experience with Julia and/or C++ 
• Experience with OpenMM and small molecule libraries (RDKit, OpenFF, DeepChem or other)
• Willingness to travel less than 15% to conferences, offsites, customers, and internal meetings.

The US base salary range for this full-time position is expected to be $150k - $210k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.