Computational Chemistry & AI/ML Scientist
inSync Staffing · Indianapolis, IN · Remote
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In its favour
- Still listed on the employer's own board
Job Title
Computational Chemistry & AI/ML Scientist
Location
Remote
Industry
Life Sciences
Job Description
Theoris Services is assisting our client in their search for a Computational Chemistry & AI/ML Scientist to support advanced AI-driven molecular design and drug discovery initiatives within a life sciences research environment. This role focuses on developing and applying state-of-the-art deep learning, graph neural networks (GNNs), geometric deep learning, and generative AI models to accelerate molecular discovery, predict chemical properties, and optimize novel compounds.
The ideal candidate combines a strong foundation in computational chemistry with hands-on expertise in PyTorch or JAX, molecular graph learning, and generative modeling techniques, including diffusion models, flow matching, autoregressive approaches, and reinforcement learning.
This individual will collaborate with multidisciplinary scientific teams to develop innovative computational approaches that advance molecular design and drug discovery.
Requirements
- Ph.D. in Computational Chemistry, Cheminformatics, Chemistry, Computer Science, Data Science, or a related field, with 1–3 years of relevant academic or industry experience; or an equivalent combination of education and experience.
- Strong deep learning fundamentals and hands-on experience with PyTorch or JAX, including working knowledge of transformers and generative modeling.
- Expertise in graph neural networks (GNNs) and geometric deep learning for molecular applications.
- Expertise in generative modeling techniques, including diffusion/flow matching, autoregressive modeling, or reinforcement learning, applied to de novo molecular design, scaffold hopping, and lead optimization under multiparameter constraints.
- Experience developing, validating, and deploying AI/ML models for chemical or molecular property prediction and design.
- Demonstrated knowledge of computational chemistry methods, including molecular modeling, molecular dynamics, and quantum mechanical calculations.
- Strong Python programming skills with relevant scientific computing and cheminformatics packages such as RDKit, Scikit-learn, PyTorch, TensorFlow, Pandas, and NumPy.
- Familiarity with molecular modeling and simulation tools such as Schrödinger Suite, MOE, AMBER, GROMACS, NAMD, or VMD.
- Experience working in Linux and high-performance computing environments.
- Strong analytical, problem-solving, and communication skills.
- Ability to work independently and effectively collaborate within multidisciplinary research teams.
Preferred Skills
- Experience in pharmaceutical, biotechnology, or related life sciences environments.
- Knowledge of drug discovery workflows, including ligand-based and structure-based drug design, lead optimization, and ADMET modeling.
- Experience with cloud computing platforms such as AWS and scalable AI/ML training workflows.
- Publications, patents, or conference presentations related to computational chemistry, generative AI, molecular machine learning, or drug discovery.
Best-In-Class-Benefits
We are in the people business; treating people right is our ONLY priority. Theoris Services consultants are full-time employees with full benefits, including:
- Robust Health Insurance
- 401(k) plan
- PTO accrual
- Paid holidays
- Excellent cash-based referral program
About Theoris
Our goal is to Fuel Your Career! As a Theoris team member, you join a culture based on people-centered values and an environment that fosters both personal and professional growth. We build long-term relationships with our clients and our consultants. With over 30 years of building strong relationships in the industry, we’re uniquely positioned to make the right connections. This knowledge is used to find the right job placement. Our recruiting teams are experts dedicated to the information technology and engineering staffing space and are highly respected by our client base.
Skills
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