Location: Remote - United States only.
About the role:
Ottimate is building the AI-native future of accounts payable. Our platform processes millions of invoices across hundreds of enterprise customers, powered by a suite of ML models and agentic workflows. As Director of AI Engineering, you will own the full AI and ML layer of our product — from invoice understanding and vendor intelligence to our conversational AP Copilot and the next generation of autonomous AP agents.
This is a hands-on leadership role. You will spend at least half your time writing code, architecting systems, and driving technical decisions alongside your team. You will also set the AI roadmap, partner cross-functionally with Product, Data, and Platform Engineering, and manage a distributed team of 8–10 engineers across Data and ML.
We are looking for a senior technical manager or director — ideally someone who has thrived at a smaller company and is ready for a career step up into broader ownership. If you are energized by shipping real AI products, working with noisy real-world financial data, and building the systems that will define how enterprises automate AP, this role is for you.
Responsibilities
Technical Leadership
- Architect and ship production AI/ML systems — you write code, not just review it
- Own the AI roadmap end-to-end: prioritization, trade-offs, delivery
- Set technical standards for model quality, evals, observability, and reliability
- Drive adoption of agentic coding tools to multiply team velocity
- Claude Code, Cursor, Copilot, or equivalent — measure and improve PR throughput
- Partner with Platform Engineering on infrastructure, data pipelines, and APIs
People & Cross-Functional
- Manage a distributed team of 8–10 engineers across Data and ML disciplines
- Hire, develop, and retain engineers at all levels; build a high-trust remote culture
- Partner with Product on roadmap sequencing and scope trade-offs
- Work directly with customer-facing teams to close feedback loops on model quality
- Communicate AI capabilities and limitations clearly to non-technical stakeholders
Model & Systems Ownership
- Own model performance metrics and drive continuous improvement pipelines
- Build and maintain evals frameworks — regression suites, human review, A/B testing
- Oversee training data collection, curation, and labeling operations
- Manage the full ML lifecycle: experimentation, deployment, monitoring, iteration
- Define and enforce quality bars for agentic workflows entering production