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About This Role
Company Description
Cricut® empowers people to make and personalize almost anything—from custom cards and apparel to everyday items and home décor. Our smart cutting machines, design apps, and materials make creativity easy and accessible for everyone. We believe everyone is born creative, and our mission is to put the power of handmade into the hands of all. With a passionate community of Makers around the world, Cricut helps turn inspiration into real, tangible creations—one project at a time.
Let’s make.
Job Description
We are seeking an experienced AI Engineering Lead/Manager to lead a team that builds and ships AI-powered features across our client-facing applications. You will bring hands-on experience integrating AI capabilities, such as AI image generation, agentic actions, and Model Context Protocol (MCP) integrations, into production software used by customers. This role blends technical leadership, hands-on engineering, and strong business sense: you will partner closely with Product Management to define features and shape the roadmap, then lead your team in turning that vision into reliable, delightful experiences.
Job Description
- Lead and grow a team of engineers building AI-powered features into our client applications, setting technical direction and a high bar for quality.
- Partner closely with Product Management to define AI features and shape the product roadmap, balancing customer value, business impact, technical feasibility, and cost.
- Architect and deliver end-to-end integrations of AI capabilities into client-facing software (e.g., AI image generation, agentic actions and workflows, and MCP-based tool and data integrations).
- Stay hands-on: write, review, and ship production code, and help your team work through the hardest technical problems.
- Evaluate models, vendors, and APIs (hosted and open-source), making pragmatic build-vs-buy recommendations based on quality, latency, cost, and reliability.
- Establish practices for evaluating and monitoring AI features, including evals, quality metrics, guardrails, and user feedback loops.
- Build AI experiences that are safe and trustworthy, addressing content safety, privacy, prompt injection, and responsible AI considerations.
- Collaborate with UX on intuitive interaction patterns for AI features, including handling latency, uncertainty, and failures gracefully.
- Work with data and ML partners to understand model training, fine-tuning, and data pipelines, and translate those capabilities into product opportunities.
- Ship behind feature flags with progressive rollout and kill-switches, validating new behavior with production telemetry before ramping.
- Own the health of AI features through telemetry by tracking adoption, output quality, latency, cost per request, and defect trends.
- Champion agentic coding tools in your team's daily workflow (AI-assisted code review, automated test generation) while maintaining code quality, security, and human oversight.
- Mentor engineers, lead effective code reviews, and foster a culture of learning and experimentation.
- Stay current on the rapidly evolving AI landscape and recommend adoption where it would meaningfully improve our products or team.
Qualifications
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
- 5+ years of hands-on, professional software development experience in one or more of: Angular, Kotlin, Swift, C#, or Python.
- 2+ years of experience as a technical lead or engineering manager, leading engineers to deliver customer-facing software.
- Proven experience integrating AI-based features into client-facing production software, such as AI image generation, agentic actions, LLM-powered experiences, or MCP (Model Context Protocol) integrations.
- Practical experience with AI/LLM APIs and SDKs (e.g., OpenAI, Anthropic, Google Gemini, Stability AI, or open-source models), including prompt design, tool/function calling, and structured outputs.
- Solid understanding of production AI tradeoffs, including latency, cost, reliability, evaluation, and safety.
- Strong business sense, with a track record of partnering with Product to define features, prioritize a roadmap, and measure outcomes.
- Experience designing and consuming REST APIs and integrating client applications with backend services.
- Experience with automated testing (unit, integration, end-to-end) and CI/CD pipelines.
- Experience instrumenting applications with telemetry/observability tools (e.g., Datadog, Firebase, or platform-native logging).
- Hands-on, pragmatic use of modern AI engineering tools (Claude Code, Cursor, Copilot, etc.).
Nice to Have
- Knowledge of machine learning concepts, such as how models are trained, fine-tuned, and evaluated, and how data pipelines support them.
- Experience with retrieval-augmented generation (RAG), embeddings, or vector databases.
- Experience with computer vision, such as image classification, object detection, segmentation, or image understanding.
- Experience with creative or design-focused applications, including 2D graphics or image processing.
Soft Skills
- Communicates clearly with engineering, product, design, and business stakeholders, and can explain AI capabilities and limitations to non-technical audiences.
- Brings strong product and business judgment, connecting technical decisions to customer value and business outcomes.
- Builds alignment on scope, priorities, and tradeoffs with Product Management and UX.
- Leads by example, mentors engineers, and helps the team grow.
- Stays curious about the fast-moving AI landscape and brings practical improvements to the team.
Additional Information We’ve Got You Covered
At Cricut, we take care of our people. Enjoy competitive Medical, Dental, and Vision coverage, a 401(k) match, generous PTO, tuition reimbursement, and a yearly lifestyle stipend to support your wellness and passions. You’ll also receive exclusive employee discounts—and best of all, you’ll be surrounded by some of the most talented, creative, and curious minds out there.
A Quick Note Before You Apply…
Cricut is in an exciting chapter of transformation. We’re evolving fast—refining our strategy, growing our teams, and raising the bar across everything we do. This is an incredible opportunity for the right kind of person—but it’s not for everyone.
We’re looking for A-players—people who thrive in dynamic environments, turn challenges into momentum, and consistently deliver their best work. If that sounds like you, read on.
Here’s what makes someone a great fit for this role (and for this moment at Cricut):
- You move with urgency. You don’t wait for perfect clarity to act—you start, learn, and adjust.
- You set high standards. You take ownership, deliver quality, and hold yourself accountable.
- You stay focused when things move fast. You prioritize what matters most and tune out the noise.
- You collaborate like a pro. You elevate others, communicate clearly, and bring a low-ego, high-output energy.
- You embrace AI as part of your toolkit. From idea exploration to data analysis and creative problem-solving, you leverage AI to accelerate innovation and amplify impact—because technology and creativity go hand-in-hand here.
One More Thing (It’s a Big One)
This role is in-office at least 4–5 days per week. We believe real collaboration, innovation, and culture are built face-to-face. If you’re energized by working alongside smart, kind, creative people—and love those hallway conversations that spark the next great idea—you’ll feel right at home.
If you’re looking for a fully remote role, this may not be the right fit. But if you’re excited by challenge, purpose, and building something better—let’s make something amazing together.
Relocation Statement:
- This position is eligible for relocation assistance.
What to Do Next: Please attach your resume, cover letter and/or include links to your portfolio or other social presence. If you want to show your super powers in other ways – include that information too. You can be sure that Cricut® is an employer who values individuality, equality and diversity, so tell us what you’re all about. If you are a Maker or a DIY enthusiast, whether you think you are a good one or not, we would love to hear about it when you send us your information.
Cricut® is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. This position is contingent on successfully completing a Criminal Background Check upon hire. Cricut participates in E-Verify.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cricut, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Cricut AI Hiring
Cricut has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in South Jordan, UT, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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