Senior AI Developer

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀

We're looking for a highly skilled and experienced Senior AI Engineer to join our ClickUp Engineering team, with a focus on AI evaluations . You'll own how we measure and improve the quality of ClickUp's AI: building the eval systems that tell us how well our AI is performing, the core agent harness, and the AI that performs well on those evals. You'll work on backend systems that make AI-powered capabilities scalable, reliable, and secure, while working hands-on with LLMs to solve real user problems and drive product innovation.

AI Processing Notice

ClickUp may use artificial intelligence and machine learning technologies to help review and screen candidates' employment applications against role-related criteria. These tools support, but do not replace, human decision‑making. If you have questions or need an accommodation in the recruitment process, please contact us at AskPeople@ClickUp.com.

Responsibilities

Design, build, and own ClickUp's AI evaluation system , including golden datasets, LLM-as-judge and automated scorers, regression detection, and quality tracking across AI features.; Build and evolve the core agent harness , and the AI that performs well on those evals, partnering closely with product and engineering to define what "great" looks like.; Build self-improvement loops where agents modify other agents or prompts, rerun evals, and iterate automatically.; Benchmark new models as they're released and own model selection, routing, and fallback strategies across providers (e.g., OpenAI, Anthropic, Google, open-weight), balancing quality, latency, and cost.; Architect, design, and implement scalable AI platform services that support the deployment, orchestration, and lifecycle management of LLMs and other AI models.; Build and maintain robust APIs and backend systems that integrate AI-powered features into ClickUp's core platform.; Develop infrastructure for model serving, monitoring, logging, and automated evaluation to ensure high reliability and performance of AI services in production.; Drive best practices in AI privacy, security, and compliance, including data anonymization, secure data handling, and regulatory adherence.; Collaborate cross-functionally with product, frontend, and data teams to deliver seamless, reliable, and user-centric AI experiences.

Requirements

5+ years of software engineering experience, including 2+ years building production LLM or ML systems.; Hands-on experience building LLM evaluation systems in production (golden datasets, LLM-as-judge, judge calibration, offline and online evals), tied to real product quality.; Strong experiment design and statistical rigor, including A/B testing, offline vs. online metrics, and separating real regressions from noisy or variable results.; Proven track record of applying LLMs and AI models to real-world, customer-facing product features.; Proven experience integrating and managing multiple LLMs, including benchmarking, routing, and cost/latency/quality tradeoffs.; Deep expertise in backend engineering, distributed systems, and cloud-native technologies (e.g., Kubernetes, Docker, AWS/GCP/Azure).; Strong programming skills in Python (preferred), Go, TypeScript, or similar languages used for backend and AI platform development.; Proficiency in orchestration frameworks and workflow engines (e.g., LangGraph, Airflow, Ray, or similar).; Familiarity with search and retrieval technologies and their integration into AI-driven applications.; Excellent collaboration and communication skills, with a track record of working effectively in cross-functional teams.; Passion for staying at the forefront of AI and applying new technologies to solve real-world problems at scale.

Nice to have

Background in data science, applied science, or machine learning engineering.; Experience with eval tooling (e.g., Braintrust, LangSmith, Weave, Arize, or in-house frameworks).; Experience with model routing, AI gateways, or inference optimization.