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Lehi, Utah, United States
Hybrid
Pattern Labs is Pattern's AI Research & Development team a small, high-autonomy group of software and AI engineers building the systems that make Pattern's ecommerce acceleration platform AI-native. A sample of what we're building today:
An AI content engine that generates, audits, and optimizes marketplace product content at scale pairing LLM generation with deterministic compliance gates and evaluation loops so output is reliable enough to ship on behalf of global brands.
Multi-agent research and insights pipelines that mine Pattern's behavioral dataset of more than 91 trillion data points to surface trends, opportunities, and recommendations for brands.
Product-data normalization systems that turn messy, multi-marketplace catalog data into a clean store of record across 60+ global marketplaces.
Multimodal image intelligence automated tagging, search, and retrieval across large brand imagery libraries.
In this role, you will work directly with the Director of Product AI R&D to define objectives and features against ambiguous business problems and from there, you'll have the autonomy to scope, design, build, evaluate, and ship end-to-end in collaboration with the rest of the team. This is a role for engineers who want room to explore: you'll have a real opportunity to conduct research, run experiments, and then execute on your findings. You will take ownership of the end-to-end delivery of full-stack applications, drive AWS architecture and execution, and champion the technical integration of AI into core processes. Working in an environment with a dedicated budget for bleeding-edge technology, you will mentor colleagues and build scalable systems that directly impact global ecommerce acceleration.
Architect, build, and deploy high-performing products from proof-of-concept through production in short, iterative cycles.
Perform heavy cloud infrastructure design and execution, leveraging AWS services to support high-throughput, data-intensive systems.
Integrate generative AI capabilities (LLMs, embeddings, multimodal models, agents) to address real business challenges and open new market opportunities.
Make AI systems production-reliable: design evaluation harnesses, guardrails, and structured outputs so LLM-powered features ship with confidence.
Work agentically: agentic coding tools (e.g., Claude Code, Cursor) are a core part of our workflow you'll help evaluate emerging AI models and set the team's standards for agentic development, automated testing, DevOps, and observability.
Guide engineering colleagues through architectural reviews, code reviews, pair programming, and technical goal setting.
Collaborate cross-functionally across global time zones, partnering directly with product management, design, and engineering teams.
5+ years of professional software engineering experience delivering production full-stack applications.
Deep familiarity and hands-on expertise with AWS infrastructure; holding an AWS Solutions Architect Certification (or the ability to achieve certification rapidly) is strongly preferred.
Proficiency in back-end development (e.g., Python/FastAPI, Node.js) and modern front-end frameworks (e.g., React).
Strong working knowledge of relational databases and SQL, with specific preference for PostgreSQL or Snowflake.
Proven track record of taking 0→1 products or complex cloud initiatives from concept through production launch.
Practical experience or high capability with generative AI systems (e.g., prompt and context engineering, vector search and retrieval, agent orchestration) and agentic development tools (e.g., Claude Code, Cursor, Copilot).
The ability to speak credibly to what it takes to make LLM systems reliable evaluation, guardrails, structured outputs, and failure modes. Prior production LLM experience isn't required, but building this discipline is a core expectation of the role.
Deliver scalable, end-to-end features on time with high code quality, reliable CI/CD pipelines, robust observability, and rigorous evaluation of AI-powered behavior.
Proactively identify system bottlenecks and architect secure, resilient AWS cloud solutions that minimize technical debt.
Elevate team productivity through thoughtful technical mentorship, fostering an environment of continuous learning and AI adoption.
Consistently align technical deliverables with product goals, translating complex AI capabilities into tangible business value.
Opportunity to spearhead architectural decisions for next-generation, AI-driven ecommerce products.
Clear advancement paths toward principal-level engineering leadership or specialized AI/Cloud architectural roles.
Exposure to executive leadership, high-impact R&D initiatives, and direct influence over engineering best practices.
Access to a dedicated education and hardware stipend, along with an AI exploration budget to continuously expand your technical skill set.
30 Days: Onboard onto the codebase and AWS environment, complete initial code contributions, and establish working relationships with R&D leadership and global engineering partners.
60 Days: Take full ownership of key feature deliverables, implement AWS best practices within your service domain, and begin integrating/evaluating new AI developer tools.
90 Days: Lead the design and deployment of an end-to-end AI/full-stack initiative with evaluation built in mentor junior team members, and contribute directly to setting architectural standards.
This role sits on Pattern Labs, Pattern's AI Research & Development team, and reports directly to the Director of Product AI R&D. You will be joining a growing team of software and AI engineering professionals based in the US and India. In this role, you will collaborate closely with Product Managers, Designers, Software Engineers, and AI Engineers. This position is mentored by the Principal Software Engineer on the team as well as other senior technical leaders.
We are looking for individuals who are:
Game Changers — A game changer is someone who looks at problems with an open mind and shares new ideas with team members, regularly reassesses existing plans and attaches a realistic timeline to goals, makes profitable, productive, and innovative contributions, and actively pursues improvements to Pattern's processes and outcomes.
Data Fanatics — A data fanatic is someone who recognizes problems and seeks to understand them through data, draws unbiased conclusions based on data that lead to actionable solutions, and continues to track the effects of the solutions using data.
Partner Obsessed — An individual who is partner obsessed clearly explains the status of projects to partners and relies on constructive feedback, actively listens to partner's expectations, and delivers results that exceed them, prioritizes the needs of your partners, and takes the time to create a personable experience for those interacting with Pattern.
Team of Doers — Someone who is a part of a team of doers uplifts team members and recognizes their specific contributions, takes initiative to help in any circumstance, actively contributes to supporting improvements, and holds themselves accountable to the team as well as to partners.
Initial phone interview with Pattern's talent acquisition team
Video interview with a hiring manager
Onsite interview with a panel of department leaders
Professional reference checks
Executive review
Offer
Demonstrate an AWS Solutions Architect Certification or highlight complex AWS production architectures you have personally designed and deployed.
Highlight hands-on experience with serverless patterns, event-driven architectures, vector databases, or LLM/agent integration and how you evaluated whether it actually worked.
Frame past engineering accomplishments with clear, data-driven outcomes and metrics showing business impact.
Show how you already use agentic coding tools in your daily work and how you review and verify what they produce.
Show enthusiasm for mentoring junior engineers and contributing to open-source or cutting-edge AI technology.
Clearly communicate your technical decisions during system design discussions, detailing how you balance immediate speed with long-term architecture.
