About The Position
Established nearly two centuries ago, FM is a leading mutual insurance company whose capital, scientific research capability and engineering expertise are solely dedicated to property risk management and the resilience of its policyholder-owners. These owners, who share the belief that the majority of property loss is preventable, represent many of the world’s largest organizations, including one of every four Fortune 500 companies. They work with FM to better understand the hazards that can impact their business continuity to make cost-effective risk management decisions, combining property loss prevention with insurance protection. Job Summary: FM Research is seeking a motivated and competent candidate as a summer intern or co-op student to leverage recent developments in Machine Learning (ML) and Artificial Intelligence (AI) to enable early anomaly detection and automated decision-making in the context of fire protection of large, high-value occupancies. We anticipate the successful candidate will be a graduate student in a technical field (e.g., computer science, data science, engineering, applied mathematics, etc.). The candidate need not be familiar with fire dynamics.
Requirements
- PhD students, with good academic standing for graduation
- Proficiency in Python programming, manipulating data structures and API interactions.
- Strong ML fundamentals (e.g., supervised/unsupervised learning, network architectures).
- Strong grasp of NLP fundamentals, LLM manipulation and the agentic loop.
- Comfortable using agentic tools (e.g., Claude Code, Codex) in code development.
- Comfortable finding, modifying and utilizing open-source datasets and repositories.
- Proficiency in using Linux systems and working on the command line (e.g., git, bash, vim).
- Strong problem-solving and communication skills.
Responsibilities
- Identify and develop a sensor fusion approach applicable to highly heterogeneous, occupancy-specific industrial sensors under the supervision of senior scientists, and employ available public datasets, synthetic data and FM-internal datasets to train and evaluate the approach for proof of concept.
- Develop a reliable early anomaly detection approach applicable across different occupancies and initiating events using the fused sensor data and demonstrate robustness to practical issues such as noise and sensor dropout.
- Time permitting, develop an LLM-based agent to demonstrate explainable and situationally-aware decision-making based on the detected anomaly and occupancy specifics.
- Document the internship achievements and present the results for internal review.
- In collaboration with senior scientists, further refine and develop the scope of the project to achieve ongoing impact beyond the scope of the internship.
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
Number of Employees
1,001-5,000 employees
