Associate Director, AI/ML Engineering
Acadia Pharmaceuticals
August 06, 2026
Remote friendly (South San Francisco, 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.
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 to enable grounded, context-aware, action-oriented AI solutions.
- Partner cross-functionally to define data contracts, lineage standards, and quality thresholds.
- Design and implement agentic memory systems and planning/reasoning loops for reliable autonomous execution.
- Evaluate agentic system performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build guardrail frameworks (input/output filtering, moderation, policy enforcement, hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector store selection, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for domain-specific pharma use cases.
- Design/deploy traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Build and maintain end-to-end ML/LLM Ops pipelines (model registry, evaluation, prompt/version control, observability, rollback).
- Experience with real-world data (RWD), claims, EHR, clinical study, translational/biological data is a plus.
Education/Experience/Skills:
- Master’s or PhD in ML, CS, Data Science, Information Systems, or related quantitative field.
- 7+ years AI/ML engineering, including 3+ years hands-on Generative AI and agentic AI.
- Expertise with multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers; integrating AI with enterprise data sources/APIs/tools.
- Strong RAG, embeddings, and vector database experience.
- Proficient in Python; PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with ML Ops/LLM Ops (lifecycle management, evaluation, deployment).
- Ability to travel domestically and internationally as required.
Benefits:
- Medical, dental, and vision insurance; 401(k) with fully vested 1:1 match up to 5%; employee stock purchase plan; paid vacation (15+ days), sick time (10 days), and paid holidays (13–15); paid parental leave; tuition assistance.
Salary Range:
- $172,000—$215,000 USD
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.
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 to enable grounded, context-aware, action-oriented AI solutions.
- Partner cross-functionally to define data contracts, lineage standards, and quality thresholds.
- Design and implement agentic memory systems and planning/reasoning loops for reliable autonomous execution.
- Evaluate agentic system performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build guardrail frameworks (input/output filtering, moderation, policy enforcement, hallucination detection).
- Develop RAG pipelines (chunking, embeddings, vector store selection, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for domain-specific pharma use cases.
- Design/deploy traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Build and maintain end-to-end ML/LLM Ops pipelines (model registry, evaluation, prompt/version control, observability, rollback).
- Experience with real-world data (RWD), claims, EHR, clinical study, translational/biological data is a plus.
Education/Experience/Skills:
- Master’s or PhD in ML, CS, Data Science, Information Systems, or related quantitative field.
- 7+ years AI/ML engineering, including 3+ years hands-on Generative AI and agentic AI.
- Expertise with multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers; integrating AI with enterprise data sources/APIs/tools.
- Strong RAG, embeddings, and vector database experience.
- Proficient in Python; PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with ML Ops/LLM Ops (lifecycle management, evaluation, deployment).
- Ability to travel domestically and internationally as required.
Benefits:
- Medical, dental, and vision insurance; 401(k) with fully vested 1:1 match up to 5%; employee stock purchase plan; paid vacation (15+ days), sick time (10 days), and paid holidays (13–15); paid parental leave; tuition assistance.
Salary Range:
- $172,000—$215,000 USD