Gradient AI:
Gradient AI is revolutionizing Group Health and P&C insurance with AI-powered solutions that help insurers predict risk more accurately, improve profitability, and automate underwriting and claims. Our SaaS platform taps into one of the industry’s largest data lakes—tens of millions of policies and claims—to deliver deep, actionable insights. Trusted by leading carriers, MGAs, TPAs, and self-insured employers, Gradient AI has grown rapidly since our founding in 2018. Backed by $56M in Series C funding, we're scaling fast—and it's an exciting time to join the team.
About the Role:
We are looking for a Data Engineer to join our Technical Implementations & Solutions Delivery team to assist with the design, build, and deployment of data pipelines for new customer implementations and to support existing customer activity.
This is an ideal role for anyone looking to dive into health data and make a real impact. You’ll work closely with our engineering and client teams to ensure smooth data integration and top-notch customer support. You’ll also help shape our vision and play a key role in transforming the entire industry (really!)
This is a fully remote opportunity.
How you will make an impact:
- Design, build, and implement data systems that fuel our ML and AI models
- Build and maintain infrastructure for the extraction, transformation, and loading (ETL) of data from a variety of sources using Python, SQL, Airflow, AWS, and healthcare-specific big data technologies and analytics platforms.
- Innovate new tools to quickly extract, process, and validate client data from different sources and platforms.
- Work cross functionally with data scientists to transform large amounts of data and store it in a format to facilitate modeling.
- Apply health and bioinformatics expertise to design data pipelines that translate complex medical concepts into actionable requirements.
Skills needed to succeed:
- BS in Computer Science (or in another quantitative discipline)
- 3+ years of experience implementing, managing, or optimizing data solutions in a professional setting
- 1+ years of experience using data orchestration frameworks such as Airflow, Dagster, Prefect
- Exposure working with a cloud-computing environment (e.g. AWS EC2)
- Comfortable with Linux, including developing shell scripts
- Willingness to learn and adapt to new tools (e.g., Redshift, Tableau, AWS Lambda)
- Experience with distributed platforms such as Apache Spark (PySpark), Databricks, or Snowflake is a bonus
- Exposure to Health, Insurance, Tech or related industry experience specifically working with AI/ML products is a huge bonus
What We Offer:
- A fun, team-oriented startup culture.
- Generous stock options - we all get to own a piece of what we’re building.
- Unlimited vacation days.
- Flexible schedule that supports working from home.
- Full benefits package includes medical, dental, vision, 401k, paid paternal leave, and more.
- Ample opportunities to learn and take on new responsibilities.
We are an equal opportunity employer.
#LI-REMOTE
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What We Do
Gradient AI is revolutionizing Group Health and P&C insurance with AI-powered solutions that help insurers predict risk more accurately, improve profitability, and automate underwriting and claims. Our SaaS platform taps into one of the industry’s largest data lakes—tens of millions of policies and claims—to deliver deep, actionable insights.
Trusted by leading carriers, MGAs, TPAs, and self-insured employers, Gradient AI has grown rapidly since our founding in 2018. Backed by $56M in Series C funding, we're scaling fast—and it's an exciting time to join the team.
Why Work With Us
Gradient is a fast-growing startup where you can make an impact, learn constantly, and grow quickly. We’re bringing AI and machine learning into the insurance space in new ways, and every team member plays a role in that innovation.
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Gradient AI Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Working from the office is optional if you are local to our Boston office. For employees who do come in, it’s usually T/W/Th to focus on group collaboration.
