Applied AI Engineer

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 and LLMs 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 Ph.D. is an advantage but not required Experience: Strong understanding of NLP, LLMs, entity recognition, and evaluation techniques. Experience designing and using embedding-based representations, including similarity search and retrieval workflows. Hands-on experience building agentic workflows in production environments. Proven experience working with unstructured data (e.g., free text, documents, notes) and applying techniques to transform it into structured formats. Skills: Strong programming skills in Python. Ability to clearly communicate complex technical concepts. Experience with data visualization. Strong analytical and problem-solving skills. Ability to work in a dynamic, fast-paced environment. Preferred: Experience in the healthcare or biotech industry. Experience with clinical data. Experience with large-scale data processing.