AI Engineer

Arbital Health; San Francisco (CA); Hybrid; USD 120,000 - 200,000; Full time

Job summary

Arbital Health is seeking an AI Engineer to help build retrieval and evaluation pipelines for our AI-powered VBC platform. You will ground model responses in production data and work closely with senior engineers to deliver reliable, scalable capabilities for healthcare contracts.

You will develop in Python and SQL, explore new AI frameworks, and contribute to prompt engineering, testing, and automated evaluation to improve customer workflows.

Qualifications

• Experience developing and deploying data-intensive AI products on SaaS platforms.
• Proficiency in Python and SQL.
• Ability to learn new frameworks and tools in AI development.
• Hands-on experience with prompt engineering, model testing, and automated evaluation for conversational AI.
• Commitment to rigorous testing and improving user workflows.

Responsibilities

• Build retrieval and evaluation pipelines and knowledge systems grounding models in production.
• Collaborate with senior engineers to improve AI tooling and customer experience.
• Prototype and deploy AI features on a SaaS platform.
• Advance AI architecture for interacting with unstructured healthcare data with real-time analytics.

Skills

Python

SQL

Prompt engineering

Model testing

Automated evaluation

AI development

Job description

Arbital Health is a rapidly growing healthcare technology and actuarial leader that centralizes, measures, and adjudicates value-based care contracts at scale. We enable payers and providers to design, measure, and execute value-based agreements with greater transparency, efficiency, and financial predictability. We invest in hiring high potential and humble individuals who thrive in fast-paced environments and can rapidly grow their responsibilities as we continue to accelerate our growth. We were co-founded by Brian Overstreet and Travis May (founder & former CEO of LiveRamp and Datavant, the two biggest data companies of the last 20 years), and are backed by Transformation Capital, Valtruis and other leading investors. In our first 2 years, Arbital Health has established itself as a trusted partner for over 40 payers, providers, and other stakeholders looking to navigate the complexities of risk-based contracting.

Arbital AI allows users to interact with complex VBC contracts & performance data in natural language and visualize it on demand.

The 1st AI-powered platform built for VBC by industry leading healthcare actuaries, Arbital AI makes complex contract performance data conversational, transparent, and instantly actionable.

We are looking for an AI Engineer to help build our next-generation conversational AI experiences. This role sits at the intersection of AI models and the data systems behind them: you'll build retrieval and evaluation pipelines and the knowledge systems that ground our models' responses in production, working alongside senior engineers while keeping the customer experience front and center.

Is this role right for you? If you want to make an impact on the US healthcare system, the idea of Arbital AI excites you, and the following matches your skills, experience, and interests:

• Development and deployment of a data-intensive AI product on a SaaS platform
• Programming in Python and SQL
• Learning new frameworks, technologies, tools, and techniques in AI development
• Hands-on experience with prompt engineering, model testing, and automated evaluation techniques for conversational AI.
• You strive for technically sound solutions that enhance user workflows, demonstrating a talent for iterative improvement and rigorous testing

Impact: As an AI Engineer at Arbital Health, you'll play a pivotal role in shaping the future of value-based healthcare through intelligent automation. You'll help unlock insights from complex financial, clinical, and actuarial data by:

• Help advancing our AI architecture for interacting with complex and unstructured healthcare contract & financial data, delivering real-time analytics and visualizations
• Driving the evaluation and production-scale deployment of LLMs within a HIPAA-compliant environment
• Dramatically reducing analytics time for strategic decision-making