Associate Director, AI/ML Engineering
Acadia Pharmaceuticals
August 07, 2026
Remote friendly (San Diego, CA)
United States
IT
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. Ensures safe, reliable deployment through robust evaluation and guardrail frameworks in a regulated biopharmaceutical environment.
Primary Responsibilities:
- Design, build, and deploy agentic AI workflows using multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
- Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities for AI agents.
- Connect multi-agent systems to enterprise databases, internal APIs, and MCP servers for grounded, context-aware, action-oriented solutions.
- Define data contracts, lineage standards, and quality thresholds for AI/ML use cases.
- Design agentic memory systems (short-term, long-term, episodic) and planning/reasoning loops.
- Evaluate performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build guardrail frameworks (filtering, moderation, policy enforcement, hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Use prompt engineering, few-shot learning, and fine-tuning for domain-specific pharma use cases.
- Develop, validate, and deploy traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Maintain end-to-end ML/LLM Ops pipelines (model registry, evaluation, prompt/version control, observability, rollback).
Qualifications/Experience/Skills:
- Master’s or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering; 3+ years hands-on Generative AI/agentic AI.
- Expertise with multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers and integrating AI with enterprise data/APIs/tools.
- Strong RAG development, embeddings, and vector database experience.
- Python; PyTorch, TensorFlow, scikit-learn, Hugging Face.
- ML Ops/LLM Ops practices (lifecycle management, evaluation, deployment).
- Travel domestically/internationally as required.
- Plus: real-world data (RWD), claims/EHR/clinical study/translational & biological data.
Benefits (as stated):
- Competitive base, discretionary bonus, and equity awards; medical/dental/vision; life/disability/travel/EAP; 401(k) match up to 5%; ESPP; 15+ vacation days; paid holidays; paid sick time; paid parental leave; tuition assistance.
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. Ensures safe, reliable deployment through robust evaluation and guardrail frameworks in a regulated biopharmaceutical environment.
Primary Responsibilities:
- Design, build, and deploy agentic AI workflows using multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
- Architect and implement MCP servers to expose enterprise tools, APIs, and data sources as standardized capabilities for AI agents.
- Connect multi-agent systems to enterprise databases, internal APIs, and MCP servers for grounded, context-aware, action-oriented solutions.
- Define data contracts, lineage standards, and quality thresholds for AI/ML use cases.
- Design agentic memory systems (short-term, long-term, episodic) and planning/reasoning loops.
- Evaluate performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build guardrail frameworks (filtering, moderation, policy enforcement, hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Use prompt engineering, few-shot learning, and fine-tuning for domain-specific pharma use cases.
- Develop, validate, and deploy traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Maintain end-to-end ML/LLM Ops pipelines (model registry, evaluation, prompt/version control, observability, rollback).
Qualifications/Experience/Skills:
- Master’s or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering; 3+ years hands-on Generative AI/agentic AI.
- Expertise with multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers and integrating AI with enterprise data/APIs/tools.
- Strong RAG development, embeddings, and vector database experience.
- Python; PyTorch, TensorFlow, scikit-learn, Hugging Face.
- ML Ops/LLM Ops practices (lifecycle management, evaluation, deployment).
- Travel domestically/internationally as required.
- Plus: real-world data (RWD), claims/EHR/clinical study/translational & biological data.
Benefits (as stated):
- Competitive base, discretionary bonus, and equity awards; medical/dental/vision; life/disability/travel/EAP; 401(k) match up to 5%; ESPP; 15+ vacation days; paid holidays; paid sick time; paid parental leave; tuition assistance.