Location: Remote - United States only.
About the role: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.
ResponsibilitiesTechnical 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
RequirementsApplied AI & Agentic Systems- Production agentic pipelines using frontier models
- Anthropic SDK • OpenAI SDK • tool use, function calling, multi-agent orchestration
- Reliable agent loop design - planning, memory, tool execution, error recovery
- RAG pipeline design - chunking, embedding models, retrieval tuning, reranking
- Evals frameworks built from scratch - correctness, regression, semantic similarity
- Observability for production AI - tracing, cost tracking, latency, failure analysis
Model Expertise- Fine-tuning frontier or open-source models for domain-specific tasks
- LoRA, QLoRA, instruction tuning - not just off-the-shelf API calls
- Training data collection, curation, cleaning, and labeling at scale
- LLM inference and serving optimization
- vLLM, TGI, or equivalent
- Model selection trade-offs - cost, latency, capability, context window
Engineering Depth- Hands-on Python - comfortable writing, reviewing, and shipping production code
- PostgreSQL - schema design, query optimization, indexing strategies
- Distributed systems - async workers, queues, retries, state machines
- Celery or similar async task frameworks is a bonus
- Public-facing API design - REST, versioning, developer experience
- MCP server development - tool-accessible APIs for AI agent integration
- AWS or cloud infrastructure - enough to own AI workload deployments
Ideal Career Background- Engineering Manager ready for director-level ownership
- Has led technical teams at a startup or growth-stage company - knows how to move fast
- Hands-on contributor who has also managed small high performance teams.
- Comfortable owning outcomes
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Industry Experience- Finance and/or AP domain - invoice workflows, GL coding, vendor management
- Hospitality - high-volume, multi-location AP operations
- Noisy, unstructured text data - OCR outputs, inconsistent supplier formats, entity resolution at scale
• Industry experience above is a bonus
Exceptional AI engineering skills are the primary bar. We will teach the domain.
BenefitsThe specific benefits/perks we offer are continually evolving, but currently include:
- Compensation: 200,000-225,000 + 15% Annual Bonus
- Competitive salary based on skills & experience.
- Medical, Dental, Vision and other Company-Subsidized Benefits for you and your family.
- Employer sponsored 401(k) with company match.
- Paid Time Off (and the encouragement to use it).
- Annual company retreats.
- Promote from within philosophy.
Beyond the tangible benefits though:
- You will be part of a growing team, at a pinnacle moment of scale for the business, and experience the excitement of working in a startup where each action makes a huge difference.
- You will have the agency to solve difficult problems creatively, the freedom to explore work that inspires you, and infrastructure to ensure you're constantly challenged and developing.
- You will work with sharp, passionate teammates solving some of the most unique challenges and positioning our product as a premier finance automation solution.
