All remote jobs

Senior AI Developer

AI Summary
ClickUp seeks a senior AI engineer to own production evaluation systems for AI product quality and to evolve its core agent harness. The role combines hands-on LLM engineering with backend platform development, including model routing, serving, monitoring, APIs, and secure scalable infrastructure. The engineer will create golden datasets, automated and LLM-based scoring, regression detection, and self-improving agent loops. Success requires rigorous experimentation, distributed-systems expertise, and close collaboration with product, frontend, and data teams. This is a high-impact senior engineering position focused on reliable customer-facing AI capabilities.
Remote from
USA
Salary
USD 200k–250k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
9 Dec 2026
Listing views
21
Application actions
2
Application toolkit

Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

Opportunity details

About this role.

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.

Key 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.

Qualifications:

  • 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.

#LI-REMOTE

#LI-AK2
#LI-CC1

Equal Opportunity Employer

ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.

Privacy Notice

ClickUp collects and processes personal data in accordance with applicable data protection laws. You can find further details by viewing our Global Candidate Privacy Notice.

If you are a Philippine Job Applicant, please also see our Philippine Data Privacy Notice for further details.

Visa Sponsorship

Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions.

Fraud Alert

ClickUp Talent Acquisition will only initiate contact via an @clickup.com email or through our official careers portal on clickup.com. We will never request fees, payments, or sensitive personal information. Please disregard any offers received outside these channels and report them to support@clickup.com.

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.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe position demands deep production experience across LLM evaluation, agent systems, experimentation, distributed backend services, and cloud infrastructure. It also requires independently setting quality standards and making complex model quality, latency, cost, privacy, and reliability trade-offs.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$225,000
US market range (per year)$190k–$270k
AI insightThe employer discloses a yearly base-pay range of $200,000–$250,000 USD, with a midpoint of $225,000. Estimated US market compensation for a senior AI engineering role with production LLM evaluation and platform responsibilities is approximately $190,000–$270,000 USD per year.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design an LLM evaluation system for a customer-facing AI feature?

I would begin by defining user-critical tasks and measurable quality criteria, then build a versioned golden dataset that represents expected, edge-case, and failure scenarios. I would combine deterministic checks, calibrated LLM judges, and human review for ambiguous cases, track metrics by feature and cohort, and gate releases on statistically meaningful regression thresholds.

How do you distinguish a real quality regression from noisy evaluation results?

I establish baselines, confidence intervals, minimum sample sizes, and task-level metric breakdowns before making decisions. I compare offline findings with online product signals where possible, inspect representative failures, and use controlled experiments to validate that the observed change is attributable to the model, prompt, or system change.

What factors would guide your model-routing strategy across multiple LLM providers?

I would route according to task complexity, quality targets, latency budgets, token cost, availability, data-handling requirements, and provider reliability. The routing layer should support benchmarking, fallbacks, observability, versioned policies, and rapid rollback when production metrics decline.

Describe how you would safely implement self-improving agent loops.

I would isolate proposed agent or prompt changes in a controlled environment, evaluate them against fixed and adversarial test suites, and require predefined quality and safety thresholds before promotion. Every iteration should be versioned, auditable, rate-limited, and reversible, with human approval for high-impact changes.

How would you make an AI platform reliable in production?

I would use resilient service design with timeouts, retries, circuit breakers, provider fallbacks, queueing where appropriate, and clear SLOs. I would add structured logging, tracing, model and prompt version tracking, cost monitoring, evaluation telemetry, and security controls for sensitive data.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Next step

Apply now.

Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information.

Did you apply?Let us know, and we’ll help you track your application.

Continue on the employer website

Protect your personal information and never pay to secure an interview or job offer. .

Log in to save
One quick step before you apply

Sign in to continue.

Sign in or create a free account to continue to the employer's application.

Add alert
Jobs Talent AI Tools Salaries
Menu