Senior Manager, AI Engineering

About Acadia Pharmaceuticals Acadia is committed to turning scientific promise into meaningful innovation that makes the difference for underserved neurological and rare disease communities around the world. Our commercial portfolio includes the first and only FDA-approved treatments for Parkinson’s disease psychosis and Rett syndrome. We are developing the next wave of therapeutic advancements with a robust and diverse pipeline that includes mid- to late-stage programs in Alzheimer’s disease psychosis and Lewy body dementia psychosis, along with earlier-stage programs that address other underserved patient needs. At Acadia, we’re here to be their difference.

Please note that this position can be based in San Diego, CA, San Francisco, CA or Princeton, NJ. Acadia’s hybrid model requires this role to work in our office three days per week on average.

Position Summary The Senior Manager, AI Engineering plays a key role in advancing Acadia’s enterprise AI capabilities through the development, deployment, and governance of scalable AI and machine learning solutions. As part of the Artificial Intelligence team, this individual will partner with stakeholders across R&D, Commercial, and Corporate functions to translate data into actionable insights and business value. This role will help drive AI strategy execution, support responsible AI practices, and enable the adoption of innovative technologies that accelerate decision-making and operational excellence across the organization.

Primary Responsibilities

• Contribute to the execution of the enterprise AI strategy and roadmap by aligning AI and machine learning initiatives with business priorities and identifying high-value use cases across the organization
• Partner with stakeholders across R&D, Commercial, and Corporate Functions to identify and develop AI and machine learning solutions that drive business value
• Deliver analytics and data science use cases that generate actionable insights and support data-driven decision-making
• Support cross-functional AI governance activities by providing technical expertise on model risk, responsible AI practices, and lifecycle management
• Assess the technical feasibility, business value, and implementation risks of proposed AI and data science initiatives to support prioritization and investment decisions
• Design, develop, validate, deploy, and support machine learning models, statistical analyses, and scalable ML/LLM pipelines using established ML Ops and LLM Ops practices
• Evaluate, implement, and optimize GenAI technologies, including large language models, vector databases, model endpoints, agent frameworks, and guardrail solutions aligned with enterprise architecture standards
• Maintain model, prompt, and dataset documentation, ensuring lineage, auditability, governance compliance, approvals, and adherence to enterprise AI system-of-record standards
• Develop reusable data science frameworks, reference implementations, and technical standards that accelerate AI adoption, scalability, and operational efficiency across the enterprise
• Partner with Information Technology, Data Insights & Analytics, and Information Security teams to support AI platforms, infrastructure capabilities, technology selection, vendor assessments, and RFI/RFP evaluations
• Apply AI governance and responsible AI standards, including model lifecycle management, bias and robustness testing, explainability, human oversight, incident response, and regulatory compliance requirements
• Support compliance with applicable GxP requirements and evolving AI regulations, including alignment with frameworks such as NIST AI RMF and EU AI Act readiness standards
• Contribute to AI enablement initiatives through knowledge sharing, technical documentation, practitioner education, portfolio reviews, and cross-functional collaboration that promotes responsible AI adoption
• Monitor emerging AI, machine learning, and regulatory developments to inform solution design, platform evolution, and enterprise AI capabilities
• Provide technical recommendations on AI solution architecture, platform strategy, and build-versus-buy decisions
• Other duties as assigned

Education/Experience/Skills

• Master’s degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative discipline with 5+ years of relevant experience; OR PhD in a related quantitative discipline with 2+ years of industry experience or …