About the role
About Our Company Built on over four decades of pioneering research at Princeton University, our platform represents the leading edge of innovation in freight and transportation planning. We help customers unlock double-digit revenue gains and drive smarter, data-driven operations at scale. With the recent close of our Series C funding round led by Koch Disruptive Technologies, we’re entering an exciting new phase of growth. Today, Optimal Dynamics is a high-growth company of ~70 employees, backed by top-tier investors including Bessemer Venture Partners, The Westly Group, Activate Capital, and Koch. We're on a mission to redefine the way logistics decisions are made—and we’re just getting started. About Our Team We are a team of bright, kind, and solution-oriented people focused on creating value for our customers. We can solve problems individually, but understand that the best solutions are found when
the team brainstorms ideas together. We are excited about balancing the need to deploy new solutions quickly and designing solutions that are secured, reliable, maintainable, and scalable for the long run.
About
The Role We’re looking for a Senior AI Engineer to lead the 0→1 buildout and 1→n scaling of AI capabilities at Optimal Dynamics. You’ll set direction, establish engineering standards, and deliver production systems that ground AI outputs in trustworthy company and customer data. This is a hands-on role with broad ownership where you’ll make key architecture and tooling choices and evolve them as we learn. Key
Responsibilities: Own RAG initiatives end-to-end from problem framing and data readiness through prototyping, iteration, and production launch. Establish foundational components and practice s for document processing, indexing, retrieval, orchestration, and evaluation; selecting tools and approaches that balance quality, cost, and speed. Build reliable services and APIs that are observable, secure, and designed for scale in a cloud environment. Define success metrics (quality, latency, cost, safety) and drive continuous improvement via experimentation and data-driven decisions. Create durable team assets (i.e. playbooks, test harnesses, checklists, and documentation) to make RAG development repeatable across products. Collaborate cross-functionally with Product, Data/ML, Engineering, and GTM to translate ambiguous needs into shippable capabilities with clear business impact. Mentor teammates and contribute to a strong engineering culture around AI systems and responsible deployment.