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 Parkinsons 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 Alzheimers disease psychosis and Lewy body dementia psychosis along with earlier-stage programs that address other underserved patient needs. At Acadia were here to be their difference.
Please note that this position is based in San Diego CA South San Francisco CA or Princeton NJ. Acadias 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
Masters 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 domestically and internationally as required
Physical Requirements
This role involves regular standing walking sitting and the use of hands for handling or operating equipment. The employee may also need to reach climb balance stoop kneel crouch and maintain visual verbal and auditory communication in a standard office environment and while working independently from remote locations. The employee must occasionally lift and/or move up to 20 pounds. This position requires the ability to travel independently overnight and/or work after hours as required by travel schedules or business needs.
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Director
About Acadia PharmaceuticalsAcadia 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 Parkinsons d...
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 Parkinsons 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 Alzheimers disease psychosis and Lewy body dementia psychosis along with earlier-stage programs that address other underserved patient needs. At Acadia were here to be their difference.
Please note that this position is based in San Diego CA South San Francisco CA or Princeton NJ. Acadias 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
Masters 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 domestically and internationally as required
Physical Requirements
This role involves regular standing walking sitting and the use of hands for handling or operating equipment. The employee may also need to reach climb balance stoop kneel crouch and maintain visual verbal and auditory communication in a standard office environment and while working independently from remote locations. The employee must occasionally lift and/or move up to 20 pounds. This position requires the ability to travel independently overnight and/or work after hours as required by travel schedules or business needs.
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