Director of AI
EPM Scientific is supporting a research‑driven technology organization building AI systems for complex scientific and clinical development workflows. This team develops production‑grade machine learning and agent‑based systems embedded in enterprise platforms, used in regulated, real‑world environments where reliability and data quality are critical.
Their work connects modern AI, including generative models and workflow‑aware agents, with real scientific and clinical feedback loops, enabling AI systems that directly support high‑impact, decision‑driven processes beyond experimentation. This role is ideal for someone who wants to work at the intersection of applied machine learning, agentic AI, and real‑world workflow integration, and who values deploying AI systems that are trusted and used at scale.
Role Overview:
You will lead the design and development of ML models and agent systems that support complex scientific and clinical workflows, partnering closely with engineers and domain experts to translate real operational needs into robust, production AI solutions.
What You'll Do:
- Own end-to-end AI strategy, translating business and product objectives into technical direction and execution.
- Architect and operationalize production-grade AI systems spanning data pipelines, model training, deployment, and monitoring.
- Lead development of advanced ML capabilities, including generative AI, agentic systems, and reasoning-driven models.
- Establish technical standards for model lifecycle management, validation, and governance in regulated environments.
- Build, mentor, and lead a high-performing AI team while partnering closely with engineering, product, and domain experts.
What You Bring:
- Advanced degree (MS or PhD) in Computer Science, Engineering, Data Science, or a related quantitative field.
- 7+ years building and deploying AI/ML systems, including ownership of architecture and technical direction in senior roles.
- Deep expertise in machine learning fundamentals, modern model architectures, and production AI systems.
- Strong background in data architecture, cloud-native platforms, and large-scale data ecosystems.
- Proven ability to lead complex, cross-functional technical initiatives and communicate effectively with senior stakeholders.