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About This Role
Location
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United States
Employment Type
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Full time
Location Type
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Remote
Department
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Engineering
Compensation
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- Estimated Base Compensation $160K – $210K
Salary and Benefits
*At ClickUp, we believe in transparency and fairness in compensation. The range displayed reflects the minimum and maximum target salaries for the position across* *all* *US locations. Please note that the actual compensation for this position may vary and is dependent on factors such as geographic location, interview performance, years of experience, education level, and specific skills. We encourage candidates to discuss compensation expectations during the interview process to ensure alignment with their qualifications and our company’s compensation philosophy.*
*This position is eligible for the following benefits and perks:*
- *Equity*
- *401k*
- *Health, Dental, and Vision insurance*
- *Spending accounts*
- *Life \& Disability*
- *Paid parental leave*
- *Flexible paid time off*
- *Enhanced employee assistance program*
- *Employee wellness stipend*
- *Professional development stipend*
OverviewApplication
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.
ClickUp is building the world's most complete platform for work by bringing tasks, docs, chat, search, and AI into one connected experience. We're hiring a Senior Backend Engineer \- AI Product to join the AI Engineering team and build the backend services and product features that power ClickUp's next\-generation AI capabilities.
This is a backend product engineering role at its core, but with one key difference: you'll be building AI\-powered product experiences directly, not internal tooling or abstract platform layers. You'll ship features end\-to\-end in support of an AI team that is deep on coding agents, multi\-agent orchestration, and production AI systems. We're looking for someone who is AI\-native (you live in this space, you get the patterns, you're excited about what's possible) but whose strength is shipping reliable, scalable backend product code that delivers real user value.
Own and ship the backend product work that brings ClickUp's AI\-driven experiences to life, ensuring the features our users interact with are fast, reliable, and well\-architected.
### What success looks like
- Ship backend product features that power AI\-driven user experiences at scale: agent interactions, intelligent workflows, and context\-aware product capabilities.
- Build services that are reliable, observable, and tightly integrated with the product surface.
- Partner closely with AI engineers, product managers, and designers to translate product vision into production\-ready backend implementations.
- Move fast without breaking things: balance iteration speed with code quality and system reliability.
- Help the team make smart tradeoffs between feature velocity, system durability, and user experience.
### Key outcomes and responsibilities
- Design and implement backend APIs, services, and data flows that power user\-facing AI product features.
- Own features end\-to\-end: from technical design through implementation, testing, rollout, and production monitoring.
- Build the backend logic for agent\-driven product experiences: context retrieval, action execution, conversation management, and intelligent routing.
- Work directly with product and design to scope, estimate, and deliver features that users interact with daily.
- Drive reliability and quality: observability, incident response, performance tuning, and rollout safety for AI\-serving product surfaces.
- Identify product\-level bottlenecks and propose pragmatic backend solutions that improve the user experience.
- Contribute to architecture decisions that balance product velocity with long\-term maintainability.
### Required qualifications
- 5\+ years of backend engineering experience building and shipping user\-facing product features at meaningful scale.
- Strong fundamentals in API design, service architecture, data modeling, and performance optimization.
- Experience working directly with or in support of AI/ML systems in production (model integration, inference pipelines, or agent\-driven features).
- Product\-minded: you care about what the user experiences, not just what the system does internally.
- Comfort working in ambiguous, fast\-moving environments where the AI landscape is shifting constantly.
- Ability to break down product requirements into clean technical implementations and ship them iteratively.
- AI\-native mindset: you're not just adjacent to AI, you're genuinely excited about agents, LLMs, and building the product experiences that make them useful.
### Preferred qualifications
- Experience building backend features for agent\-based or AI\-powered product experiences.
- Familiarity with LLM integration patterns: RAG, function calling, tool use, prompt routing, context management.
- Experience with event\-driven or asynchronous architectures.
- Proficiency in TypeScript/Node.js, Python, or Go.
- Experience in SaaS, productivity, or developer\-platform environments.
- Familiarity with modern AI patterns: vector databases, model gateways, inference optimization, orchestration frameworks.
- Background at a startup or high\-growth company where you shipped product fast and wore multiple hats.
### Location
- Fully remote
\#LI\-REMOTE
\#LI\-AK2
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.*
Compensation Range: $160K \- $210K
Salary Context
This $160K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ClickUp, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($185K) sits 15% below the category median. Disclosed range: $160K to $210K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
ClickUp AI Hiring
ClickUp has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $210K - $210K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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