Verisk

Builds data and risk software for insurers

Senior Software Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeBoston, MA, USA$125k - $150k

About the Job

Requirements

A B.S. in Computer Science, Mathematics, Statistics, or a related quantitative field is required.
At least 5 years of software engineering experience is required, including at least 2 years in a senior or lead role on cloud-native AWS products.
Strong Python skills for data engineering, machine learning pipelines, and API development are required.
Hands-on experience with machine learning frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost is required.
Experience building and deploying production machine learning systems, including model training, evaluation, versioning, and serving, is required.
Proficiency with AWS data and AI services, including SageMaker, S3, Glue, Athena, Lambda, EC2, and CloudWatch, is required.
Experience with data pipeline tooling such as Apache Spark, Airflow, dbt, or equivalent is required.
A solid understanding of data modeling and SQL, and experience working with large-scale databases such as PostgreSQL, MSSQL, or similar, is required.
A strong grasp of software engineering fundamentals, including CI/CD, DevOps, testing, and system design, is required.
Familiarity with REST API design, microservices, and containerization tools such as Docker and Kubernetes is required.
Experience with Agile development methodologies is required.

Responsibilities

Design, build, and deploy machine learning models and AI-powered features into production SaaS products.
Maintain scalable data pipelines for ingesting, transforming, and enriching large, complex datasets.
Develop model-serving infrastructure using AWS SageMaker, Lambda, and container-based deployment patterns.
Apply LLM integrations, RAG architectures, and generative AI capabilities where appropriate to enhance product functionality.
Own data quality, observability, and monitoring for AI/ML workloads in production.
Lead the design and implementation of cloud-native microservices and APIs using Python and C#/.NET on AWS.
Drive best practices in design, code quality, and system design across the team.
Contribute to all stages of the software development life cycle, including requirements review, design, development, testing, and deployment.
Conduct code reviews and mentor team members on engineering standards.
Proactively identify technical risks and communicate them early to course-correct.
Participate in roadmap planning, scoping, and technology feasibility assessments.
Contribute to a culture where solving customer problems is the highest priority.

Desired Qualifications

A master's degree or Ph.D. is preferred.
Experience with LLMs, prompt engineering, or RAG systems is preferred.
Familiarity with MLflow, Weights & Biases, or other machine learning lifecycle management tools is preferred.
An AWS certification such as Machine Learning Specialty, Solutions Architect, or equivalent is preferred.
Experience with geospatial data, catastrophe modeling, or climate/weather datasets is preferred.
Full-stack experience with Angular or React and .NET Core is preferred.
A background in the insurance, reinsurance, or financial services industries is preferred.

We are hiring a Senior Software Engineer with deep expertise in AI/ML engineering and data-intensive systems to join our Catastrophic and Risk Solutions team. You will be a key technical contributor on a cross-functional Agile team building cloud-native SaaS platforms that sit at the intersection of cutting-edge science and production software. This role goes beyond traditional full-stack development — you will design and ship AI-powered features, build data pipelines, and architect scalable ML-serving infrastructure on AWS. This role is office-based in our Boston location, which has a flexible hybrid work model.

Required

B.S. in Computer Science, Mathematics, Statistics, or a related quantitative field; M.S. or Ph.D. preferred
5+ years of software engineering experience, with at least 2 years in a senior or lead role on cloud-native AWS products
Strong Python skills for data engineering, ML pipelines, and API development
Hands-on experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost
Experience building and deploying production ML systems — model training, evaluation, versioning, and serving
Proficiency with AWS data and AI services: SageMaker, S3, Glue, Athena, Lambda, EC2, CloudWatch
Experience with data pipeline tooling: Apache Spark, Airflow, dbt, or equivalent
Solid understanding of data modeling, SQL, and working with large-scale databases (PostgreSQL, MSSQL, or similar)
Strong grasp of software engineering fundamentals: CI/CD, DevOps, testing, and system design
Familiarity with REST API design, microservices, and containerization (Docker, Kubernetes)
Experience with Agile development methodologies

Nice to Have

Experience with LLMs, prompt engineering, or RAG (Retrieval-Augmented Generation) systems
Familiarity with MLflow, Weights & Biases, or other ML lifecycle management tools
AWS Certification (Machine Learning Specialty, Solutions Architect, or equivalent)
Experience with geospatial data, catastrophe modeling, or climate/weather datasets
Full-stack experience with Angular or React and .NET Core
Background in the insurance, reinsurance, or financial services industries

#LI-LM03
#LI-Hybrid

AI & Data Engineering

Design, build, and deploy machine learning models and AI-powered features into production SaaS products
Maintain scalable data pipelines for ingestion, transformation, and enrichment of large, complex datasets
Develop model-serving infrastructure using AWS SageMaker, Lambda, and container-based deployment patterns
Apply LLM integrations, RAG architectures, and generative AI capabilities where appropriate to enhance product functionality
Own data quality, observability, and monitoring for AI/ML workloads in production

Software Engineering & Architecture

Lead the design and implementation of cloud-native microservices and APIs (Python, C#/.NET) on AWS
Drive best practices in design, code quality, and system design across the team
Contribute to all stages of the SDLC: requirements review, design, development, testing, and deployment
Conduct code reviews and mentor team members on engineering standards
Proactively identify technical risks and communicate them early to course-correct
Participate in roadmap planning, scoping, and technology feasibility assessments
Contribute to a culture where solving customer problems is always the highest priority

Budgeted Salary Currency: USD

Minimum Salary: 125,000

Maximum Salary: 150,000

About the Company

Verisk

Builds data and risk software for insurers

Verisk sells data, analytics, and software to insurers, reinsurers, and insurance intermediaries that assess risk and settle claims. Its tools support insurance pricing and policy development, catastrophe modeling, and claims investigation, combining industry records with scientific models and specialized applications. Xactimate calculates property repair estimates, while other platforms compare claims to identify suspected fraud and model losses from natural disasters. Customers pay through hosted subscriptions, transaction fees, and advisory services, with software and data integrated into their underwriting and claims workflows. Verisk traces its operating roots to 1971 and is headquartered in Jersey City, New Jersey.

Company size: 5,001-10,000
Company stage: IPO
Headquarters: Jersey City, NJ
Founded: 1971

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