Associate Director, AI/ML 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 is based in San Diego, CA, South 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 Associate Director, AI/ML Engineering serves as a hands-on technical leader driving the design, architecture, and delivery of Generative AI and agentic AI solutions across the enterprise. This role builds scalable multi-agent systems, connects AI solutions to enterprise data and tools, and ensures safe, reliable deployment through robust evaluation and guardrail frameworks. The position also applies strong machine learning and foundation model expertise to deliver high-impact use cases within a regulated biopharmaceutical environment.

Primary Responsibilities

• Design, build, and deploy agentic AI workflows that automate and transform complex business processes, leveraging multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or equivalent).
• Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities consumable by AI agents.
• Connect multi-agent systems to enterprise databases, internal APIs, and MCP servers to enable grounded, context-aware, and action-oriented AI solutions.
• Partner cross-functionally with internal teams to define data contracts, lineage standards, and quality thresholds required for AI/ML use cases.
• Design and implement agentic memory systems (short-term, long-term, episodic) and planning/reasoning loops to support reliable autonomous task execution.
• Evaluate agentic system performance across accuracy, reliability, latency, cost, and safety dimensions using structured benchmarks and red-teaming methodologies.
• Build and maintain guardrail frameworks (input/output filtering, content moderation, policy enforcement, hallucination detection) to ensure the safety, compliance, and trustworthiness of GenAI and agentic solutions.
• Develop retrieval-augmented generation (RAG) pipelines, including chunking strategies, embedding models, vector store selection, and retrieval optimization for enterprise knowledge bases.
• Apply prompt engineering, few-shot learning, and fine-tuning techniques to adapt foundation models for domain-specific pharma use cases.
• Design, develop, validate, and deploy traditional machine learning models (classification, regression, clustering, time-series, survival analysis) to address structured business problems.
• Build and maintain end-to-end ML pipelines adhering to LLM Ops / ML Ops standards including model registry, evaluation benchmarks, prompt/version control, observability, and rollback procedures.
• Experience in working with real-world data (RWD), claims data, EHR data, Clinical Study data, translational and biological data and the corresponding databases is a plus.
• Other responsibilities as assigned.

Education/Experience/Skills

• Master’s or PhD in Machine Learning, Computer Science, Data Science, Information Systems, or a related quantitative discipline
• Minimum of 7 years of experience in AI/ML engineering, including at least 3 years of hands-on experience with Generative AI and agentic AI systems
• Expertise in multi-agent frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar technologies
• Experience building MCP servers and integrating AI systems with enterprise data sources, APIs, and tools
• Strong experience in RAG pipeline development, embedding models, and vector database technologies
• Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face
• Experience implementing ML Ops or LLM Ops practices, including model lifecycle management, evaluation, and deployment
• Ability to travel domesticall…