Conduct research on reasoning, planning, memory, and generalization architectures for AI agents and multi-agent systems.
Develop and test frameworks for agent cognition, including abstraction, goal decomposition, and adaptive planning.
Contribute to the lab's understanding of foundational principles underlying intelligent behavior in artificial systems.
Stay current with academic literature and industry advances in AI reasoning, cognitive architectures, and agent design.
Design and execute benchmarking experiments to evaluate agent reasoning, reliability, and generalization capabilities.
Develop evaluation methodologies for assessing AI agent performance across diverse tasks and domains.
Serve as a hands-on AI engineer, designing, developing, and deploying advanced agentic solutions.
Build multi-agent AI systems using frameworks such as Strands, LangGraph, DSPy, and similar agentic architectures.
Develop RAG (Retrieval Augmented Generation) applications, knowledge graphs, and conversational AI solutions.
Turn research concepts into working prototypes that demonstrate advances in agent intelligence.

Requirements

Educational Background: BA/BS required (quantitative area of study such as Computer Science, Data Science, Statistics, Mathematics, Cognitive Science, or Engineering preferred). MS or other graduate degree preferred.
Technical Proficiency: Proficiency in Python (R or Julia), with hands-on experience with ML frameworks (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.
Familiarity with benchmarking and evaluation methodologies for AI systems.
Proficiency in SQL and experience with cloud-based environments (AWS, Azure, GCP).
Experience with Git and software version control.
Experience with experimental design and statistical analysis preferred.
Communication Skills: Excellent communication and presentation skills, with a proven ability to explain complex research and analyses to both technical and non-technical stakeholders. Experience presenting at conferences or publishing research preferred.
Experience: A minimum of 3 years of hands-on experience in data science, machine learning, AI development, or AI research. Relevant advanced degrees contribute to required years of experience.
Experience with pharmaceutical or life sciences industry preferred.
Cross-Functional Collaboration: Proven experience in working on complex research or development projects with multi-functional team members.

Benefits

Health Coverage: Medical, pharmacy, dental, and vision care.
Wellbeing Support: Programs such as BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
Financial Well-being and Protection: 401(k) plan, short- and long-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.
Work-life benefits include: Paid Time Off US Exempt Employees: flexible time off (unlimited, with manager approval, 11 paid national holidays.
Additional time off for employees may include unlimited paid sick time, up to 2 paid volunteer days per year, summer hours flexibility, leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs and an annual Global Shutdown between Christmas and New Years Day.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonRJuliaScikit-LearnTensorFlowPyTorchSQLbenchmarking methodologiesevaluation methodologiesexperimental design

Soft Skills

communication skillspresentation skillscross-functional collaboration