Job Description

Job DescriptionJob Description Arrowhead Pharmaceuticals, Inc. (Nasdaq: ARWR) is a clinical stage biopharmaceutical company that develops medicines that treat intractable diseases by silencing the genes that cause them. Using a broad portfolio of RNA chemistries and efficient modes of delivery, Arrowhead therapies trigger the RNA interference mechanism to induce rapid, deep, and durable knockdown of target genes. RNA interference, or RNAi, is a mechanism present in living cells that inhibits the expression of a specific gene, thereby affecting the production of a specific protein. Arrowhead's RNAi-based therapeutics leverage this natural pathway of gene silencing. Arrowhead is focused on developing innovative drugs for diseases with a genetic basis, typically characterized by the overproduction of one or more proteins that are involved with disease. The depth and versatility of our RNAi technologies enables us to potentially address conditions in virtually any therapeutic area and pursue disease targets that are not otherwise addressable by small molecules and biologics. Arrowhead is leading the field in bringing the promise of RNAi to address diseases outside of the liver, and our clinical pipeline includes disease targets in the liver and lung with a promising pipeline of preclinical candidates. Arrowhead's corporate headquarters is in Pasadena, CA with research and development teams in Madison, WI and San Diego, CA. Our employees are nimble, science-driven innovators who are collaborating to bring new therapies to patients in need. The Position 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- 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. Responsibilities 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 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. Cheminformatics & Data Analysis: Develop algorithms to mine and integrate diverse datasets into rational design pipelines. 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. Algorithm Development: Build and deploy custom ML/AI frameworks to accelerate lead optimization. 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. Requirements 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. 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. 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. 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. 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. 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). Wisconsin pay range $120,000—$140,000 USD Arrowhead provides competitive salaries and an excellent benefit package. 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