Applied AI Engineer

About the role

Position Overview

PhaseV is a technology company that redefines clinical development at scale with its enterprise-ready, multi-modal AI/ML platform. With over $65M raised from top investors, we are poised to make a significant impact on how drugs are developed and brought to market - ultimately helping effective treatments reach patients faster.

We are seeking a talented and driven Applied AI Engineer to join our team. This is a unique opportunity to have a direct and real-world impact using AI by helping redefine how clinical trials are designed, optimized, and run. As an Applied AI Engineer, you will also join a high-caliber, multidisciplinary, cross-functional team spanning engineering, data science, and product.

This role sits at the intersection of applied AI and product development, with a strong focus on building, deploying, and supporting production-grade AI systems that operate at scale. The ideal candidate will have a strong foundation in NLP, LLMs, and agentic systems alongside proven experience building AI-powered services.

Key Responsibilities

Technical Development:

- Build and maintain scalable, production-grade generative AI pipelines that integrate updating unstructured and structured data.

- Research and develop robust agentic systems to solve complex clinical problems.

- Develop evaluation frameworks for AI systems in clinical contexts.

- Optimize representation and retrieval strategies to support explainable outputs.

- Contribute to architectural decisions around AI services, APIs, and model orchestration frameworks.

Research & Analysis:

- Stay up-to-date with the latest developments in AI, machine learning, and related fields, exploring how emerging technologies can be applied to improve products and services.

- Document and present methodologies and results for internal and external stakeholders.

Collaboration:

- Work cross-functionally with engineering, data science, and product teams.

- Contribute to technical discussions and peer code reviews.

- Support the preparation of technical documentation and research papers.

Qualifications

Education:

- Master's in Computer Science, Data Science, Engineering or a related field is an advantage but not required

- Ph.D. is an advantage but not required

Experience:

- Hands-on experience building agentic workflows in production environments.

- Strong understanding of NLP, LLMs, entity recognition, and evaluation techniques.

- Experience designing and using embedding-based representations, including similarity search and retrieval workflows.

- Proven experience working with unstructured data (e.g., free text, documents, notes) and applying techniques to transform it into structured formats.

Skills: