Senior AI/ML Engineer

Target RWE•Research Triangle Park, NC

8h•Hybrid

About The Position

Target RWE is seeking a Senior AI/ML Engineer to join our Technology team and help shape the next generation of real-world evidence solutions. In this hands-on role, you will design, develop, and deploy scalable machine learning systems — with a particular focus on large language model (LLM) applications. These systems enable our researchers, statisticians, and clinical teams to extract deeper insights from complex healthcare data. Your work will directly support Target RWE’s mission of advancing clinical research and informing better healthcare decisions at scale. This is a highly collaborative position. You will partner closely with software engineers, data scientists, research scientists, and product managers to build high-performing, reliable, and scalable ML-native platforms. You will also serve as a technical leader within the Engineering team, helping cultivate a strong ML engineering culture and staying at the forefront of rapidly evolving AI technologies. This role reports to the Director of Engineering. This is a role for someone who thrives at the intersection of cutting-edge AI research and production-grade engineering. The right fit is someone who can translate state-of-the-art methods into durable, enterprise-ready systems while understanding the requirements of clinical research. This is an exceptional opportunity for someone excited about applying AI to transform how clinical evidence is generated and used.

Requirements

5+ years of experience in backend and cloud platform software development, with the flexibility to traverse the stack when necessary.
Fluency in Python.
Strong familiarity with modern ML and LLM techniques, frameworks, and tooling.
Demonstrated experience developing and maintaining high performing, scalable, data-centric enterprise software products.
Strong problem-solving skills and meticulous attention to detail in technical design, implementation, and documentation.
Excellent communication skills, with the ability to clearly articulate complex technical concepts to non-technical stakeholders.
Collaborative team player who thrives in a cross-functional environment alongside engineers, researchers, and product managers.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.

Nice To Haves

Experience in 0-to-1 development of end-to-end ML systems, including design, training, inference, deployment, and monitoring.
Hands-on experience with LLMs in production environments, including fine-tuning, prompt engineering, or retrieval-augmented generation (RAG).
Experience building the plumbing necessary for RAG-based approaches, including data vectorization, vector stores, and various search methodologies.
Background in healthcare, clinical informatics, or life sciences, with familiarity with real-world clinical data sources (EHR, claims, registries).

Responsibilities

Design, develop, and deploy scalable ML software systems to address large-scale analytical challenges in healthcare and real-world evidence generation.
Translate state-of-the-art LLM research — both internally developed and from the broader research community — into production-ready solutions that deliver measurable value for our customers and internal teams.
Work with complex, large-scale, real-world clinical data (structured and unstructured) in a cloud-based environment, ensuring quality and reliability across the data pipeline.
Develop methods and features to ensure high-quality production model results, including drift detection, performance degradation monitoring, and observability tooling.
Collaborate with software engineers, research scientists, and product teams to build ML-native enterprise platforms, ensuring scalability, efficiency, and reliability at every layer.
Ensure robust monitoring, logging, and error handling for all deployed ML systems.
Stay current on the latest advancements in machine learning and AI, and proactively identify opportunities to apply relevant innovations to Target RWE’s platform.
Cultivate a strong ML engineering culture across the organization, contributing to best practices, code reviews, documentation, and technical mentorship.
Partner with cross-functional stakeholders to translate complex technical concepts and model outputs into clear, actionable insights for non-technical audiences.