Senior Generative AI Engineer

Posted about 1 month agoViewed

💎 Seniority level: Senior

💸 Salary: 220439.0 - 276101.0 USD per year

🔍 Industry: Health Tech

🏢 Company: Virta Health👥 251-500💰 $133,000,000 Series E about 4 years agoPersonal Health Medical Health Care Diabetes

🗣️ Languages: English

Requirements:

Experience with large language models (LLMs) such as OpenAI’s GPT, including prompt engineering and tuning for performance and engagement.

Strong software engineering skills, particularly in Python, with experience writing production-quality code.

Proven ability to design and build ML systems end-to-end, including experimentation frameworks, real-time pipelines, and monitoring infrastructure.

Experience deploying ML models in production environments, preferably on cloud platforms like Google Cloud Platform (GCP).

Background in building or contributing to internal platforms/tools that are used across multiple teams or products.

Familiarity with experimentation methods, including A/B testing and evaluation of conversational systems.

Comfort working cross-functionally—collaborating closely with product managers, designers, and engineering stakeholders.

Strong communication and technical leadership skills, including mentoring other engineers and aligning on technical decisions within a larger leadership team.

Track record of using data to inform iteration, particularly with user-facing ML products where engagement and outcomes are key success metrics.

Responsibilities:

Collaborate with cross-functional teams to evolve Viv, Virta’s GPT-powered chatbot, into a scalable platform.

Design and implement core infrastructure to support prompt orchestration, context management, experimentation, and evaluation at scale.

Contribute to the technical strategy for conversational AI—bringing a GenAI perspective to platform decisions, roadmap planning, and system architecture.

Write production-grade code and build robust ML systems that support continuous learning, monitoring, and iteration.

Mentor and guide teammates across disciplines, elevating engineering practices and GenAI fluency across the organization.

Measure and improve chatbot performance through data-driven iteration, focusing on member engagement, response quality, and outcome achievement.

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