Senior Manager, AI Engineering

Acadia Pharmaceuticals•San Diego, NJ

1d•Hybrid

About The Position

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.

Requirements

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 equivalent advanced research experience in machine learning, artificial intelligence, or data science
Experience developing, deploying, and supporting machine learning and AI solutions in production or research environments
Proficiency in Python, SQL, and AI/ML frameworks such as PyTorch, TensorFlow, or scikit-learn
Experience working with Generative AI technologies, large language models, retrieval-augmented generation (RAG), vector databases, and agent frameworks
Experience with ML Ops or LLM Ops practices, including version control, model evaluation, monitoring, and deployment workflows
Knowledge of responsible AI principles, model explainability, evaluation methodologies, and AI governance concepts
Experience working within regulated or compliance-focused environments preferred
Ability to travel domestically and internationally as needed

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