Interested in this AI/ML Engineer role at Cricut?
Apply Now →Skills & Technologies
About This Role
Company Description
We are seeking an experienced Lead Agentic Software Engineer, Connected Devices to build and ship features across our client applications, including iOS/iPadOS, Android, and Electron/Angular. This hands\-on, code focused role centers on writing production code and using agentic coding tools to accelerate delivery without compromising quality. The position focuses on connecting our client apps with Cricut hardware over Bluetooth, BLE, USB, and Wi\-Fi, so direct hardware\-software integration experience is essential. You will bring deep expertise in one major client platform and work closely with Product Management and UX to turn defined features into shipped experiences.
Job Description
- Dedicate most of your time to writing, reviewing, and shipping production code on your primary platform.
- Use agentic coding tools as a core part of your daily workflow — AI\-assisted code review, automated test generation, AI\-augmented development — while maintaining code quality, security, and human oversight.
- Design and build features end\-to\-end, from technical approach through implementation, testing, and release.
- Apply established architectural patterns to keep your code modular, maintainable, and testable.
- Collaborate closely with Product Management and UX to take defined features from spec to shipped experience, surfacing technical tradeoffs and feasibility early.
- Build and maintain secure, reliable communication between client applications and Cricut hardware over Bluetooth, BLE, USB, and Wi\-Fi.
- Optimize application performance — startup time, memory usage, responsiveness, rendering, battery life, and network efficiency.
- Ensure application security through encryption, secure authentication, certificate validation, and secure credential/key management.
- Write and maintain automated tests for your features.
- Drive CI/CD automation, release quality, and deployment processes for your platform (App Store, Google Play, or desktop distribution).
- Ship behind feature flags with progressive rollout and kill\-switches, validating new behavior with production telemetry before ramping.
- Own release health through telemetry — tracking crash\-free rate, performance budgets, and defect trends.
- Participate in code reviews and share knowledge with other engineers on your platform.
- Evaluate emerging platform, AI, and tooling technologies, and recommend adoption where they'd meaningfully improve your team's work.
Qualifications
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
- 8\+ years of professional client/frontend software development experience, with deep, production\-level expertise in one of: iOS, Android, or Electron/Angular.
- Strong expertise in device connectivity — Bluetooth Classic, BLE (including GATT design), USB, and Wi\-Fi (provisioning, mDNS/SSDP discovery) — along with real\-world experience handling pairing, interference, bandwidth limits, and reconnection.
- Working knowledge of embedded hardware integration: firmware constraints, command protocols, calibration, OTA updates, and error handling/telemetry.
- At least a year of hands\-on, pragmatic use of modern AI engineering tools (Claude Code, Cursor, Copilot, etc.), with practical experience integrating AI/LLM capabilities where relevant (agentic patterns, evals, latency/cost/reliability tradeoffs).
- Expert\-level proficiency in the language matching your primary platform (Swift, Kotlin, or TypeScript).
- Strong expertise in the native UI framework for your platform (SwiftUI/UIKit, Jetpack Compose, or Electron/Angular).
- Strong understanding of your platform's concurrency model (Swift Concurrency, Kotlin Coroutines/Flow, or modern asynchronous JavaScript/RxJS).
- Experience with architectural patterns such as MVVM, Clean Architecture, MVI, or component\-based architecture.
- Experience consuming REST APIs.
- Strong debugging, profiling, and performance optimization skills using platform\-native tools (Xcode Instruments, Android Profiler, or browser/Electron devtools).
- Experience with automated testing (unit, integration, UI/end\-to\-end) and CI/CD pipelines.
- Working knowledge of secure credential storage (Keychain, Android Keystore, or equivalent).
- Experience instrumenting applications with telemetry/observability tools (e.g., Datadog, Firebase, or platform\-native logging).
Soft Skills
- Communicates clearly with engineering, product, and design — written and verbal.
- Collaborates well with Product Management and UX, building alignment on scope and tradeoffs.
- Shares knowledge readily and helps other engineers on the team grow.
- Stays curious about new tools and techniques, especially in AI\-assisted development, and brings practical improvements to the team.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cricut AI Hiring
Cricut has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in South Jordan, UT, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.