Associate Director, Intelligence Systems Lab
Bristol Myers Squibb
4 hours ago
Remote friendly (Seattle, WA)
United States
IT
Responsibilities
- Lead research initiatives exploring foundational principles of intelligence, including reasoning, planning, memory, and generalization.
- Architect frameworks for agent cognition, abstraction, goal decomposition, and adaptive planning; establish design principles for multi-agent systems.
- Define research hypotheses and experimental agendas; synthesize academic and industry advances to shape the lab’s technical roadmap.
- Publish findings and represent the lab at internal forums and external conferences.
- Lead benchmarking and evaluation science for AI agents; develop rigorous methodologies and success criteria for multi-agent systems.
- Oversee creation of test harnesses, evaluation datasets, and measurement standards; ensure experimental rigor and reproducibility.
- Lead design and development of multi-agent AI systems using frameworks such as Strands, LangGraph, DSPy, and similar architectures.
- Oversee RAG applications, knowledge graphs, and conversational AI solutions; drive autonomous workflows and decision systems.
- Translate research into working prototypes and production-ready demonstrations; apply ROI-first evaluation; identify enterprise deployment opportunities.
- Partner cross-functionally across Commercial, GPS/Manufacturing, Clinical Development, and Research; collaborate with IT/data engineering/platform teams to deploy prototypes.
- Mentor and develop team members; establish governance and best practices for agent evaluation and research methodology; lead knowledge sharing.
Qualifications/Required Skills
- BA/BS required in a quantitative area (Computer Science, Data Science, Statistics, Mathematics, Cognitive Science, or Engineering); Ph.D./graduate degree preferred.
- Python proficiency (R or Julia); ML frameworks experience (Scikit-Learn, TensorFlow, PyTorch, etc.).
- Experience with agentic AI frameworks (Strands, LangGraph, AutoGen, DSPy, CrewAI, or similar).
- Experience with LLMs, prompt engineering, and RAG architectures.
- Strong benchmarking/evaluation methodology; proficiency in SQL.
- Cloud familiarity (AWS, Azure, GCP); Git, MLOps practices, and containerization.
- Strong experimental design, statistical analysis, and research methodology.
- Excellent communication/presentation skills.
Experience (Required)
- Minimum 5 years hands-on experience in data science, machine learning, AI development, or AI research.
Application Instructions
- If a role doesn’t perfectly line up with your resume, apply anyway.
- Lead research initiatives exploring foundational principles of intelligence, including reasoning, planning, memory, and generalization.
- Architect frameworks for agent cognition, abstraction, goal decomposition, and adaptive planning; establish design principles for multi-agent systems.
- Define research hypotheses and experimental agendas; synthesize academic and industry advances to shape the lab’s technical roadmap.
- Publish findings and represent the lab at internal forums and external conferences.
- Lead benchmarking and evaluation science for AI agents; develop rigorous methodologies and success criteria for multi-agent systems.
- Oversee creation of test harnesses, evaluation datasets, and measurement standards; ensure experimental rigor and reproducibility.
- Lead design and development of multi-agent AI systems using frameworks such as Strands, LangGraph, DSPy, and similar architectures.
- Oversee RAG applications, knowledge graphs, and conversational AI solutions; drive autonomous workflows and decision systems.
- Translate research into working prototypes and production-ready demonstrations; apply ROI-first evaluation; identify enterprise deployment opportunities.
- Partner cross-functionally across Commercial, GPS/Manufacturing, Clinical Development, and Research; collaborate with IT/data engineering/platform teams to deploy prototypes.
- Mentor and develop team members; establish governance and best practices for agent evaluation and research methodology; lead knowledge sharing.
Qualifications/Required Skills
- BA/BS required in a quantitative area (Computer Science, Data Science, Statistics, Mathematics, Cognitive Science, or Engineering); Ph.D./graduate degree preferred.
- Python proficiency (R or Julia); ML frameworks experience (Scikit-Learn, TensorFlow, PyTorch, etc.).
- Experience with agentic AI frameworks (Strands, LangGraph, AutoGen, DSPy, CrewAI, or similar).
- Experience with LLMs, prompt engineering, and RAG architectures.
- Strong benchmarking/evaluation methodology; proficiency in SQL.
- Cloud familiarity (AWS, Azure, GCP); Git, MLOps practices, and containerization.
- Strong experimental design, statistical analysis, and research methodology.
- Excellent communication/presentation skills.
Experience (Required)
- Minimum 5 years hands-on experience in data science, machine learning, AI development, or AI research.
Application Instructions
- If a role doesn’t perfectly line up with your resume, apply anyway.