Research Scientist
Machine Learning & Biopharma
Posted on 3/18/2024
INACTIVE
SandboxAQ

201-500 employees

Develops AI and quantum technology solutions for various industries
Company Overview
SandboxAQ, an independent entity born out of Alphabet Inc., leverages the combined potential of AI and Quantum technology to develop commercial products for various computationally-intensive sectors. The company's strength lies in its unique approach of integrating diverse fields like physics, computer science, neuroscience, and more, fostering a culture of experimental thinking and collaboration that results in breakthrough solutions. Committed to education and talent development, SandboxAQ invests in future talent through various initiatives, including internships, research papers, developer tools, and partnerships with universities, aiming to prepare individuals for the quantum era and promote STEM careers.
Data & Analytics
Cybersecurity
AI & Machine Learning
Financial Services
B2B

Company Stage

Series D

Total Funding

$2B

Founded

2021

Headquarters

New York, New York

Growth & Insights
Headcount

6 month growth

25%

1 year growth

96%

2 year growth

362%
Locations
Remote in USA • Remote
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Microsoft Azure
Agile
Python
Tensorflow
Pytorch
AWS
Google Cloud Platform
CategoriesNew
Lab & Research
Interdisciplinary Research
Physical Sciences
Requirements
  • M.S. with 2-3 years of relevant experience or Ph.D. in computational physics, computational chemistry, computer science, or related field
  • Proficient in Python and modern software development practices
  • Experience with ML and deep learning tools (e.g., PyTorch, TensorFlow) and cheminformatics toolkits (e.g., RDKit)
  • Knowledge in deploying ML models on cloud platforms (e.g., AWS, GCP, Azure)
  • Demonstrated success in applying ML to drug discovery or materials science, with a portfolio of impactful contributions
  • Experience in one or more of the following ML applications to drug discovery or materials science: chemical space exploration, reinforcement learning, active learning, generative methods for chemistry, ML force fields, crystal structure prediction, reaction pathway prediction, QSAR, similarity search, chemical space visualization, ADME ML predictions, PK ML predictions, chemical clustering, knowledge graphs, foundation models
Responsibilities
  • Develop and improve novel deep learning methods applied to chemistry and drug discovery problems
  • Develop innovative ML methods to predict functional characteristics and properties of molecules and biological systems for rational structure-based drug design
  • Help guide and scope projects with clear deliverables alongside agile teams
  • Collaborate closely with multi-disciplinary teams to independently prototype and scale cutting-edge, impactful drug design solutions
  • Generate and evaluate hypotheses to assist design decisions and influence project direction by developing and deploying computational methods and workflows
  • Effectively present and communicate research findings through talks, blog posts, clients, and scientific publications.