Associate Director, AI/ML Engineering
Acadia Pharmaceuticals
June 24, 2026
Remote friendly (South San Francisco, CA)
United States
IT
Position Summary
Associate Director, AI/ML Engineering—hands-on technical leader designing, architecting, and delivering Generative AI and agentic AI solutions across the enterprise, including safe/reliable deployment 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 AI agent capabilities.
- Connect multi-agent systems to enterprise databases/internal APIs/MCP for grounded, context-aware, action-oriented solutions.
- Define data contracts, lineage standards, and quality thresholds with cross-functional teams.
- Implement agentic memory systems and planning/reasoning loops for reliable autonomous execution.
- Evaluate accuracy, reliability, latency, cost, and safety via benchmarks and red-teaming.
- Build guardrail frameworks (filtering/moderation/policy enforcement/hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for pharma use cases.
- Design/develop/validate/deploy traditional ML models (classification, regression, clustering, time-series, survival).
- Build/maintain ML Ops/LLM Ops pipelines (registry, evaluation, prompt/version control, observability, rollback).
Qualifications
- Master’s or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering, including 3+ years hands-on Generative AI and agentic AI.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers and integrating with enterprise data/APIs/tools.
- Strong RAG, embeddings, and vector database experience.
- Python; PyTorch/TensorFlow/scikit-learn/Hugging Face.
- Experience with ML Ops/LLM Ops lifecycle management, evaluation, and deployment.
- Ability to travel domestically/internationally (as required).
- RWD/claims/EHR/clinical/translational/biological data experience is a plus.
Benefits (as stated)
- Discretionary bonus and equity; salary range: $172,000–$215,000 USD.
- Medical/dental/vision; employer-paid life/disability/travel/EAP; 401(k) with 1:1 match up to 5%; ESPP; 15+ vacation days; 13–15 paid holidays; 10 days paid sick time; paid parental leave; tuition assistance.
Associate Director, AI/ML Engineering—hands-on technical leader designing, architecting, and delivering Generative AI and agentic AI solutions across the enterprise, including safe/reliable deployment 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 AI agent capabilities.
- Connect multi-agent systems to enterprise databases/internal APIs/MCP for grounded, context-aware, action-oriented solutions.
- Define data contracts, lineage standards, and quality thresholds with cross-functional teams.
- Implement agentic memory systems and planning/reasoning loops for reliable autonomous execution.
- Evaluate accuracy, reliability, latency, cost, and safety via benchmarks and red-teaming.
- Build guardrail frameworks (filtering/moderation/policy enforcement/hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for pharma use cases.
- Design/develop/validate/deploy traditional ML models (classification, regression, clustering, time-series, survival).
- Build/maintain ML Ops/LLM Ops pipelines (registry, evaluation, prompt/version control, observability, rollback).
Qualifications
- Master’s or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering, including 3+ years hands-on Generative AI and agentic AI.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers and integrating with enterprise data/APIs/tools.
- Strong RAG, embeddings, and vector database experience.
- Python; PyTorch/TensorFlow/scikit-learn/Hugging Face.
- Experience with ML Ops/LLM Ops lifecycle management, evaluation, and deployment.
- Ability to travel domestically/internationally (as required).
- RWD/claims/EHR/clinical/translational/biological data experience is a plus.
Benefits (as stated)
- Discretionary bonus and equity; salary range: $172,000–$215,000 USD.
- Medical/dental/vision; employer-paid life/disability/travel/EAP; 401(k) with 1:1 match up to 5%; ESPP; 15+ vacation days; 13–15 paid holidays; 10 days paid sick time; paid parental leave; tuition assistance.