• Architect Embedded Agent Cycles: Transition Mainspring from "AI-assisted" tasks to a model where autonomous and semi-autonomous AI agents are natively integrated into the engineering lifecycle—from code generation and hardware simulation to automated testing and documentation • Workflow Optimization: Identify and re-engineer high-impact engineering bottlenecks, deploying AI agents that act as functional members of the development team • ROI & Performance Measurement: Establish a data-driven framework to measure the impact of AI on engineering velocity, error reduction, and resource optimization. You will be responsible for defining and reporting on clear ROI metrics for all AI initiatives • Standardized Guardrails: Design and implement automated guardrails within the CI/CD pipeline to ensure AI-generated output meets Mainspring’s rigorous safety, security, and quality standards • Responsible Agent Execution: Establish governance frameworks for agentic behavior, including "human-in-the-loop" checkpoints, audit logs, and access controls to prevent drift or unauthorized actions in production environments • Model Evaluation: Build rigorous benchmarking systems to evaluate model reliability and cost-effectiveness, ensuring a lean and high-performing AI stack • Agentic Infrastructure: Design the end-to-end infrastructure required to support multi-agent systems, including orchestration layers, long-term memory (Vector DBs), and tool-use capabilities (RAG) • Build vs. Buy Strategy: Act as the technical authority on whether to leverage third-party AI platforms or develop proprietary agentic tools tailored to our specific hardware-software engineering needs

• Build and improve backend services that power our conversational AI platform • Develop APIs and integrations with internal systems and external data sources • Work with modern AI technologies and large language models (LLMs) in production environments • Design and implement scalable cloud infrastructure and data pipelines • Collaborate with engineers, product managers, and data scientists to ship new features • Contribute to system reliability, performance improvements, and monitoring

Role Description 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 - Applied 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 Qualifications - 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 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 Benefits - 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 Company Description We care deeply about making Ottimate an incredible place to work and invest a significant amount of time and energy into creating and maintaining a company culture that provides our team with a generous level of support whilst inspiring them to do their best work. Our commitment to empowering a diverse and inclusive workforce, celebrating differences, and creating a safe space for our employees to bring their whole selves to work is second to note. We are transforming entire industries using innovative technology including Artificial Intelligence, Payment Tech, and Neural Networks. Our leaders lead with a people-first approach; inspiring excellence, nurturing ideas, and finding creative ways to eliminate obstacles for cultivating growth. We truly love what we do and who we do it with - and we think you will too! Ottimate is an equal opportunity employer that is committed to diversity and inclusion. We do not discriminate based on race, color, national origin, religion, gender, gender expression, sexual orientation, age, veteran status, disability status, or marital status. If you are excited about the role but do not meet 100% of the qualifications listed above, we encourage you to apply. Nothing in this job posting should be construed as an offer or guarantee of employment.

• Define and execute the long-term data science strategy • Act as the primary liaison between Product Management and the Data Science team • Support the charge in migrating SurveyMonkey from a traditional SaaS product to an AI-first platform • Drive the strategy for integrating and leveraging Large Language Models and advanced Natural Language Processing techniques • Set the technical standards for model development, deployment, monitoring, and maintenance • Lead, mentor, and inspire a high-performing team of data scientists and machine learning engineers • Recruit top-tier data science talent, manage performance, and define career progression paths