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Pattern is an ecommerce acceleration platform that leverages proprietary AI and machine learning to optimize and automate growth across 60+ global marketplaces (Amazon, Walmart, Target, eBay, Tmall, TikTok Shop, JD, Mercado Libre). The company processes 91+ trillion data points to help hundreds of global brands drive profitable revenue growth through advertising optimization, content management, logistics, pricing, forecasting, and customer service automation.
Pattern Labs is the company's AI Research & Development team—a small, high-autonomy group building AI-native systems. Current projects include: an AI content engine that generates and audits marketplace product content at scale using LLMs with deterministic compliance gates; multi-agent research pipelines that surface trends and opportunities from behavioral data; product-data normalization systems across 60+ marketplaces; and multimodal image intelligence for automated tagging and retrieval.
In this role, you will work directly with the Director of Product AI R&D to define objectives against ambiguous business problems, then scope, design, build, evaluate, and ship end-to-end solutions in collaboration with the team. You'll have real autonomy to conduct research, run experiments, and execute on findings. Key responsibilities include:
- Architect, build, and deploy high-performing products from proof-of-concept through production in short, iterative cycles
- Perform heavy cloud infrastructure design and execution using AWS services to support high-throughput, data-intensive systems
- Integrate generative AI capabilities (LLMs, embeddings, multimodal models, agents) to address real business challenges
- Make AI systems production-reliable by designing evaluation harnesses, guardrails, and structured outputs so LLM-powered features ship with confidence
- Work agentically using agentic coding tools (Claude Code, Cursor) as core workflow; help evaluate emerging AI models and set team 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 with product management, design, and engineering teams
You'll take ownership of end-to-end delivery of full-stack applications, drive AWS architecture and execution, and champion technical integration of AI into core processes. Working with a dedicated budget for bleeding-edge technology, you'll mentor colleagues and build scalable systems that directly impact global ecommerce acceleration.
High performance means delivering scalable, end-to-end features on time with high code quality, reliable CI/CD pipelines, robust observability, and rigorous evaluation of AI-powered behavior. You'll proactively identify system bottlenecks, architect secure and resilient AWS solutions, elevate team productivity through mentorship, and consistently align technical deliverables with product goals.
Career growth includes opportunities 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 and high-impact R&D initiatives, and access to dedicated education and hardware stipends plus an AI exploration budget.
REQUIREMENTS:
- 5+ years of professional software engineering experience delivering production full-stack applications
- Deep familiarity and hands-on expertise with AWS infrastructure; AWS Solutions Architect Certification (or ability to achieve rapidly) strongly preferred
- Proficiency in back-end development (Python/FastAPI, Node.js) and modern front-end frameworks (React)
- Strong working knowledge of relational databases and SQL, with 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 (prompt and context engineering, vector search and retrieval, agent orchestration) and agentic development tools (Claude Code, Cursor, Copilot)
- Ability to speak credibly to making LLM systems reliable: evaluation, guardrails, structured outputs, and failure modes. Prior production LLM experience not required, but building this discipline is a core expectation.
