<p>The Position</p> <p>We are seeking a highly skilled Machine Learning Scientist with strong computational chemistry expertise to join our dynamic team in Madison, Wisconsin. In this on-site role, you will apply advanced machine-learning models to accelerate the development of targeting ligands and next-generation RNA-based therapeutics. You will work closely with a cross-functional R&D team at the cutting edge of RNA interference (RNAi), where your computational insights will directly impact the development of life-changing therapies.</p> <p>Responsibilities</p> <ul> <li>Design and Modeling: Apply state-of-the-art computational chemistry and machine learning methods to design and optimize peptide and protein ligands, predict siRNA efficacy, off-target effects, and chemical modification profile. Utilize structural modeling tools (e.g., AlphaFold, Rosetta) to predict structures and guide the engineering of novel ligands </li><li>Machine Learning R&D: Develop, train, and refine predictive models for peptide/protein/siRNA properties using deep learning techniques. Analyze large biological datasets (sequences, structures, activity data) to uncover patterns and insights that inform lead discovery. </li><li>Cheminformatics & Data Analysis: Develop algorithms to mine and integrate diverse datasets into rational design pipelines. </li><li>Cross-Functional Collaboration: Work closely with chemists, biologists, and other scientists to integrate computational designs with experimental validation. Propose candidates and provide in-silico rationale for compounds to be synthesized or biologically tested, and iteratively improve designs based on lab feedback. </li><li>Algorithm Development: Build and deploy custom ML/AI frameworks to accelerate lead optimization. </li><li>Innovation & Continuous Learning: Stay up-to-date with the latest research and advancements in AI/ML for drug discovery (e.g. new algorithms, frameworks, and scientific publications). Evaluate and integrate new tools or methodologies (for example, improved protein structure prediction algorithms or generative models) to continually enhance the team's capabilities. </li></ul> <p>Requirements</p> <ul> <li>Ph.D. in a relevant field (Computational Chemistry, Biochemistry, Bioinformatics, Computer Science, or related discipline) with a focus on computational approaches in chemistry or biology. Candidates with 0-10 years of postdoctoral or industry experience (including recent PhD graduates) are encouraged to apply. </li><li>Computational & ML Expertise: Demonstrated experience applying machine learning or AI techniques to chemical or biological problems. Strong understanding of algorithms for modeling molecular structures or properties, and familiarity with statistical modeling and data science in a scientific context. </li><li>Programming Skills: Proficiency in Python programming and common scientific computing libraries. Ability to develop and debug code for modeling workflows; experience with version control (Git) and reproducible research practices. </li><li>Collaborative Skills: Excellent problem-solving abilities and communication skills. Proven ability to work both independently and as part of an interdisciplinary team, effectively communicating computational findings to collaborators from chemistry and biology backgrounds. </li></ul> <p>Preferred</p> <ul> <li>Deep Learning Frameworks: Experience with modern deep learning frameworks such as PyTorch or TensorFlow for model development and data analysis. Familiarity with related ecosystem tools and techniques like neural networks, CNNs/RNNs or transformer models applied to molecular data. </li><li>Structural Modeling Tools: Hands-on experience with protein structure prediction and design tools (e.g., AlphaFold, Rosetta/RoseTTAFold) and understanding of their applications/limitations in therapeutic design. Experience with other bioinformatics or computational biology tools (such as RFDiffusion, ProteinMPNN, or molecular docking software). </li></ul>