Forward Deployed Engineer, AI

Arrowhead Pharmaceuticals•San Diego, CA

3d•$155,000 - $190,000

About The Position

Arrowhead Pharmaceuticals is hiring its first Forward Deployed Engineer, AI for a newly created, green-field role. The position partners directly with business and scientific stakeholders across multiple functions to identify high-value problems and build and deploy AI-driven solutions. The engineer will help establish how AI solutions are scoped, built, and delivered internally, working across the AWS, Azure, Databricks, and Microsoft technology stack with platform and engineering colleagues. The role requires comfort with ambiguity and rapid movement from problem discovery to working solutions, embedding with internal teams to understand their workflows before designing and shipping tools that fit their needs. The role focuses on building agentic AI workflows and applications, primarily by composing and integrating existing foundation models rather than training new ones. It covers the process from prototypes through production, including data access and modeling, deployment practices, monitoring, documentation, and reusable engineering standards. The position collaborates with teams across R&D, clinical, manufacturing, commercial, and other business functions.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
At least 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 with Databricks, including data engineering, notebooks, and/or ML workflows.
Strong proficiency in at least one modern programming language, such as Python or SQL, used to build data and AI applications.
Demonstrated experience building agentic workflows and generative AI/LLM-based applications, such as through orchestration frameworks, tool use, or retrieval-augmented generation, to solve practical business problems.
Ability to work independently in ambiguous, undefined environments and build solutions from the ground up without an established playbook.
Strong cross-functional communication skills and ability to translate technical concepts for non-technical stakeholders and vice versa.
Current, valid authorization to work in the country where the role is located.

Nice To Haves

Knowledge of the pharmaceutical, biotech, or life sciences industry.
Databricks, AWS, or Microsoft Azure certifications.
Prior experience in a forward deployed engineer, solutions engineering, or embedded engineering role working directly with business teams.
Experience with LLMOps/agent-ops tooling and practices for deploying, monitoring, and evaluating agentic workflows in production.
Familiarity with regulated-industry considerations, such as data privacy, validation, and GxP, relevant to deploying AI solutions in pharma.
Experience building internal tools, chatbots, or agentic AI applications using modern LLM frameworks.
A builder mindset and initiative to define scope and process in a green-field function.

Responsibilities

Partner 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 to solve operational and scientific problems, from prototype through production.
Establish foundational engineering practices, standards, and reusable patterns for internal AI solution delivery.
Work with the Data Platform Engineer to access, model, and productionize data across Databricks and AWS.
Collaborate with the DevOps Engineer to build CI/CD pipelines, deployment processes, and monitoring for AI applications across AWS and Databricks within Microsoft environments.
Use Databricks and AWS services, including Bedrock, Lambda, and S3, to build and orchestrate agentic AI workflows and applications, primarily integrating existing foundation models.
Integrate solutions with Microsoft and Azure tools, such as Azure AI services and M365, where appropriate.
Rapidly prototype proof-of-concepts with internal teams, gather feedback, and iterate to move validated solutions into production.
Document architecture, decisions, and delivery patterns to create a repeatable internal playbook.
Embed with business teams as a hands-on technical resource, translating ambiguous business problems into concrete technical solutions.