Associate Director, AI/ML Engineering
Acadia Pharmaceuticals
June 24, 2026
Remote friendly (Princeton, NJ)
United States
IT
Responsibilities:
- Design, build, and deploy agentic AI workflows using multi-agent orchestration (e.g., LangGraph, AutoGen, CrewAI).
- Architect MCP servers to expose enterprise tools/APIs/data as agent capabilities.
- Integrate multi-agent systems with enterprise databases, APIs, and MCP for grounded, context-aware actions.
- Define data contracts, lineage, and quality thresholds with cross-functional teams.
- Implement agentic memory (short/long/episodic) and planning/reasoning loops for reliable autonomy.
- Evaluate performance (accuracy, reliability, latency, cost, safety) via 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, and fine-tuning for pharma domain use cases.
- Develop/deploy ML models (classification, regression, clustering, time-series, survival).
- Maintain end-to-end ML/LLM Ops pipelines (registry, evaluations, prompt/version control, observability, rollback).
Qualifications/Skills:
- MS or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering; 3+ years hands-on GenAI/agentic AI.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, etc.).
- Experience with MCP servers and enterprise integrations.
- Strong RAG/embeddings/vector database experience.
- Python; PyTorch/TensorFlow/scikit-learn/Hugging Face.
- ML Ops/LLM Ops experience (lifecycle, evaluation, deployment).
- Preferred: real-world data (RWD), claims, EHR, clinical study, translational/biological data.
- Travel as required.
Benefits/Compensation:
- Bonus and equity awards (discretionary).
- Salary range: $172,000–$215,000 USD.
What we offer (highlights):
- Medical/dental/vision; 401(k) with 1:1 match up to 5%; employee stock purchase plan; 15+ vacation days; paid holidays (incl. office closure Dec 24–Jan 1); paid sick time; paid parental leave; tuition assistance.
- Design, build, and deploy agentic AI workflows using multi-agent orchestration (e.g., LangGraph, AutoGen, CrewAI).
- Architect MCP servers to expose enterprise tools/APIs/data as agent capabilities.
- Integrate multi-agent systems with enterprise databases, APIs, and MCP for grounded, context-aware actions.
- Define data contracts, lineage, and quality thresholds with cross-functional teams.
- Implement agentic memory (short/long/episodic) and planning/reasoning loops for reliable autonomy.
- Evaluate performance (accuracy, reliability, latency, cost, safety) via 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, and fine-tuning for pharma domain use cases.
- Develop/deploy ML models (classification, regression, clustering, time-series, survival).
- Maintain end-to-end ML/LLM Ops pipelines (registry, evaluations, prompt/version control, observability, rollback).
Qualifications/Skills:
- MS or PhD in ML/CS/Data Science/IS or related quantitative field.
- 7+ years AI/ML engineering; 3+ years hands-on GenAI/agentic AI.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, etc.).
- Experience with MCP servers and enterprise integrations.
- Strong RAG/embeddings/vector database experience.
- Python; PyTorch/TensorFlow/scikit-learn/Hugging Face.
- ML Ops/LLM Ops experience (lifecycle, evaluation, deployment).
- Preferred: real-world data (RWD), claims, EHR, clinical study, translational/biological data.
- Travel as required.
Benefits/Compensation:
- Bonus and equity awards (discretionary).
- Salary range: $172,000–$215,000 USD.
What we offer (highlights):
- Medical/dental/vision; 401(k) with 1:1 match up to 5%; employee stock purchase plan; 15+ vacation days; paid holidays (incl. office closure Dec 24–Jan 1); paid sick time; paid parental leave; tuition assistance.