Forward Deployed Engineer, AI at Arrowhead Pharmaceuticals in San Diego, CA

  • Company: Arrowhead Pharmaceuticals
  • Location: Madison, Wisconsin, United States | Pasadena, California, United States | San Diego, California, United States | Verona, Wisconsin, United States
  • Salary: $155K – $190K
  • Job type: full time
  • Workplace: onsite
  • Posted: 2026-10-20

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Job description

Arrowhead Pharmaceuticals, Inc. (Nasdaq ARWR) is a commercial 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 & San Diego, CA, and a state of the art manufacturing facility in Verona, WI. Our employees are nimble, science-driven innovators who are collaborating to bring new therapies to patients in need. The Position This is a newly created, green-field role and the first Forward Deployed Engineer, AI hired within the company. The person in this role will partner directly with business and scientific stakeholders across multiple functions to identify high-value problems and build and deploy AI-driven solutions that solve them. Because there is no existing playbook for this function, the Forward Deployed Engineer will help define how AI solutions are scoped, built, and delivered internally, working hands-on across the AWS, Azure, Databricks, and Microsoft technology stack alongside the Data Platform Engineer, DevOps Engineer, and Databricks/AWS platform teams. This role requires someone comfortable operating with ambiguity, moving quickly from problem discovery to a working solution, and embedding directly with internal teams to understand their workflows before proposing and shipping AI tools that fit how they actually work. Responsibilities Partner directly with stakeholders across R&D, clinical, manufacturing, commercial, and other business functions to identify, scope, and prioritize opportunities for AI-driven internal solutions. Design, build, and deploy agentic AI workflows and applications that solve real operational and scientific problems, working end-to-end from prototype through production. Establish foundational engineering practices, standards, and reusable patterns for AI solution delivery, since this is the first role of its kind in the organization. Work closely with the Data Platform Engineer to access, model, and productionize data needed for AI solutions across Databricks and AWS. Collaborate with the DevOps Engineer to build reliable CI/CD pipelines, deployment processes, and monitoring for AI applications across AWS/ Databricks inside a MS environments. Leverage the Databricks platform and AWS services (e.g., Bedrock, Lambda, S3) to build and orchestrate agentic AI workflows and applications, primarily by composing and integrating existing foundation models rather than training new ones. Integrate solutions with Microsoft/Azure tools (e.g., Azure AI services, M365) where appropriate to meet business needs. Rapidly prototype proof-of-concepts with internal teams, gather feedback, and iterate quickly to move validated solutions into production. Document architecture, decisions, and delivery patterns to build a repeatable internal playbook for future

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