When underwriters have real-time risk selection and portfolio insights at their fingertips, profitable growth follows!

Senior Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteTeam 11-50H1B SponsorCompany Site LinkedIn

Location

United States

Posted

45 days ago

Salary

$170K - $210K / year

5 yrs expEnglishCloud

Job Description

• Develop, implement, and validate machine learning and agentic flows to improve submission intake workflows, underwriting decision making, and related predictive/automation tasks. • Establish, maintain, and evolve the principles guiding autonomous agent behavior and decision-making across AI workflows. • Lead the design of benchmarking frameworks, evaluation tools, and metrics to continuously measure, upgrade, and optimize models, LLMs, and related systems for latency, predictive accuracy, and coverage of relevant use cases. • Build modular, reusable model architectures, training routines, and evaluation frameworks that enable rapid experimentation and adaptation to multiple insurance use cases. • Lead the research, design, and implementation of novel machine learning models and agentic workflows that drive next-generation product capabilities.

Job Requirements

Proven experience as a Applied Scientist or Machine Learning Engineer, or similar role (at least 5 years), with at least 2 years of focused experience designing, developing, and fine-tuning large language models (LLMs) and advanced ML models in real-world applications.
Proven experience designing scalable, robust, and reusable ML/LLM model architectures and evaluation frameworks, including both classical machine learning and modern generative models.
Hands-on experience with model experimentation, benchmarking, and evaluation pipelines, including monitoring model performance, drift, and generalization across use cases to ensure reliability and reproducibility.
Experience integrating models with production systems in collaboration with engineering teams, including deploying, monitoring, and iteratively improving models in cloud or hybrid environments.
Excellent communication skills, with the ability to convey complex modeling concepts, trade-offs, and findings to technical and non-technical stakeholders.