AI Engineering Lead/Manager

Cricut•South Jordan, UT

3d•Onsite

About The Position

Cricut is seeking an experienced AI Engineering Lead/Manager to lead a team that builds and ships AI-powered features across its client-facing applications. This role requires 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. The position blends technical leadership, hands-on engineering, and strong business sense, involving close partnership with Product Management to define features and shape the roadmap, followed by leading the team in developing reliable and delightful user experiences.

Requirements

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.).
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.

Nice To Haves

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.

Responsibilities

Lead and grow a team of engineers building AI-powered features into 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).
Write, review, and ship production code, and help the 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 the 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 products or the team.