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 forward-deployed engineering work. Act as a hands-on technical resource embedded with business teams, translating ambiguous business problems into concrete technical solutions. Requirements Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience. 5+ years of professional software engineering experience, including building and deploying production-grade applications. Hands-on experience with AWS cloud services and Azure/Microsoft cloud services. Experience working with Databricks, including data engineering, notebooks, and/or ML workflows. Strong proficiency in at least one modern programming language (e.g., Python, SQL, or similar) used for building data and AI applications. Demonstrated experience building agentic workflows and applications with generative AI and LLM-based approaches (e.g., orchestration frameworks, tool use, retrieval-augmented generation) to solve practical business problems. Proven ability to work independently in ambiguous, undefined environments and to build solutions from the ground up without an established playbook. Strong cross-functional communication skills, with the ability to translate technical concepts for non-technical business stakeholders and vice versa. Preferred Knowledge of the pharmaceutical, biotech, or life sciences industry is desirable but not required. Experience with Databricks certifications, AWS certifications, or Microsoft Azure certifications. Prior experience in a "forward deployed engineer," solutions engineering, or embedded engineering role, working directly with business teams rather than in a purely centralized product function. Experience with LLMOps/agent-ops tooling and practices for deploying, monitoring, and evaluating agentic workflows in production. Familiarity with regulated-industry considerations (e.g., data privacy, validation, GxP) relevant to deploying AI solutions in a pharma environment. Experience building internal tools, chatbots, or agentic AI applications using modern LLM frameworks. A builder mindset with the initiative to define scope and process in a green-field function. Wisconsin pay range $155,000 — $175,000 USD California pay range $175,000 — $190,000 USD Arrowhead provides competitive salaries and an excellent benefit package. Candidates must have current, valid authorization to work in the country where this role is located. California Applicant Privacy Policy

Requirements

Must-haves

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.
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 forward-deployed engineering work.
Act as a hands-on technical resource embedded with business teams, translating ambiguous business problems into concrete technical solutions.
Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
5+ years of professional software engineering experience, including building and deploying production-grade applications.
Hands-on experience with AWS cloud services and Azure/Microsoft cloud services.
Experience working with Databricks, including data engineering, notebooks, and/or ML workflows.
Strong proficiency in at least one modern programming language (e.g., Python, SQL, or similar) used for building data and AI applications.
Proven ability to work independently in ambiguous, undefined environments and to build solutions from the ground up without an established playbook.
Strong cross-functional communication skills, with the ability to translate technical concepts for non-technical business stakeholders and vice versa.
Knowledge of the pharmaceutical, biotech, or life sciences industry is desirable but not required.

Nice-to-haves

Experience with Databricks certifications, AWS certifications, or Microsoft Azure certifications.
Prior experience in a "forward deployed engineer," solutions engineering, or embedded engineering role, working directly with business teams rather than in a purely centralized product function.
Experience with LLMOps/agent-ops tooling and practices for deploying, monitoring, and evaluating agentic workflows in production.
Familiarity with regulated-industry considerations (e.g., data privacy, validation, GxP) relevant to deploying AI solutions in a pharma environment.
Experience building internal tools, chatbots, or agentic AI applications using modern LLM frameworks.
A builder mindset with the initiative to define scope and process in a green-field function.
California Applicant Privacy Policy

Application prep

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Platform: greenhouse
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