Associate Director, AI/ML Engineering
Acadia Pharmaceuticals
June 24, 2026
Remote friendly (San Diego, CA)
United States
IT
Primary Responsibilities:
- Design, build, and deploy agentic AI workflows to automate complex business processes 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.
- Integrate multi-agent systems with enterprise databases, internal APIs, and MCP servers for grounded, context-aware, action-oriented AI solutions.
- Partner cross-functionally to 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 for reliable autonomous execution.
- Evaluate agentic performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build guardrail frameworks (input/output filtering, moderation, policy enforcement, hallucination detection) to ensure safety and compliance.
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for domain pharma use cases.
- Design and deploy traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Build and maintain end-to-end ML pipelines with LLM Ops/ML Ops standards (registry, evals, prompt/version control, observability, rollback).
- Other responsibilities as assigned.
Qualifications/Skills:
- Master’s or PhD in Machine Learning, Computer Science, Data Science, Information Systems, or related quantitative field.
- 7+ years AI/ML engineering, including 3+ years hands-on Generative AI/agentic AI systems.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers and integrating AI with enterprise data/tools.
- Strong RAG experience (embeddings, vector databases).
- Proficiency in Python; frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with ML Ops/LLM Ops (model lifecycle, evaluation, deployment).
- Ability to travel domestically/internationally.
- Real-world data (RWD), claims, EHR, clinical study, translational/biological data is a plus.
Benefits/Compensation (if included):
- Discretionary bonus and equity awards; Salary range: $172,000–$215,000 USD.
- Medical, dental, vision; employer-paid life/disability/business travel/EAP; 401(k) match; employee stock purchase plan; 15+ vacation days; paid holidays; 10 days sick time; paid parental leave; tuition assistance.
- Design, build, and deploy agentic AI workflows to automate complex business processes 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.
- Integrate multi-agent systems with enterprise databases, internal APIs, and MCP servers for grounded, context-aware, action-oriented AI solutions.
- Partner cross-functionally to 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 for reliable autonomous execution.
- Evaluate agentic performance (accuracy, reliability, latency, cost, safety) using benchmarks and red-teaming.
- Build guardrail frameworks (input/output filtering, moderation, policy enforcement, hallucination detection) to ensure safety and compliance.
- Develop RAG pipelines (chunking, embeddings, vector stores, retrieval optimization).
- Apply prompt engineering, few-shot learning, and fine-tuning for domain pharma use cases.
- Design and deploy traditional ML models (classification, regression, clustering, time-series, survival analysis).
- Build and maintain end-to-end ML pipelines with LLM Ops/ML Ops standards (registry, evals, prompt/version control, observability, rollback).
- Other responsibilities as assigned.
Qualifications/Skills:
- Master’s or PhD in Machine Learning, Computer Science, Data Science, Information Systems, or related quantitative field.
- 7+ years AI/ML engineering, including 3+ years hands-on Generative AI/agentic AI systems.
- Expertise in multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar).
- Experience building MCP servers and integrating AI with enterprise data/tools.
- Strong RAG experience (embeddings, vector databases).
- Proficiency in Python; frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face.
- Experience with ML Ops/LLM Ops (model lifecycle, evaluation, deployment).
- Ability to travel domestically/internationally.
- Real-world data (RWD), claims, EHR, clinical study, translational/biological data is a plus.
Benefits/Compensation (if included):
- Discretionary bonus and equity awards; Salary range: $172,000–$215,000 USD.
- Medical, dental, vision; employer-paid life/disability/business travel/EAP; 401(k) match; employee stock purchase plan; 15+ vacation days; paid holidays; 10 days sick time; paid parental leave; tuition assistance.