AT KLUE https://klue.com/, WE’RE BUILDING THE FUTURE OF COMPETITIVE INTELLIGENCE 👋 KLUE ENGINEERING IS HIRING! We're looking for a Senior Software Engineer to join our team in Vancouver, someone excited to build and optimize state-of-the-art LLM-powered agents at scale. You'll bring a builder's mindset, scientific rigor, and relentless customer focus. You'll have an outsized impact on the product while staying close to the frontier of AI. You will shape how we build and operate AI agents at scale, from multi-agent orchestration and sub-agent design to the eval frameworks that keep outputs trusted and measurable. This means optimizing across the full stack: inference costs at scale, retrieval and query performance, and the feedback loops that make agents genuinely improve over time. You won't just execute on the roadmap, you'll help shape it, bringing a technical point of view on where the product should go and working closely with product leadership to get there. In this role, you will own projects end-to-end, guiding architecture decisions, experimentation strategy, and production readiness for our LLM-powered agents. WHAT YOU'LL DO - Build and ship backend systems that power agentic workflows. You design retrieval pipelines, orchestration layers, and multi-step agent architectures that turn millions of competitive data points (news, press releases, webpage changes, Slack posts, emails, reviews, CRM data) into actionable intelligence for our customers. - Improve LLM-powered workflows end to end. From prompt design and retrieval strategy to caching and latency optimization, you'll make our agent responses faster, more accurate, and more reliable in production. - Own evaluation of agentic systems at scale. You develop and operate evaluation frameworks (automated, offline, and human-in-the-loop) that measure relevance, quality, latency, and end-to-end task success across our agent pipelines. You'll define what "good" looks like and build the infrastructure to measure it continuously. - Design and build human-in-the-loop systems. Working closely with product and design, you propose and prototype feedback mechanisms, review workflows, and correction loops that keep AI agents accurate and trusted over time. You understand when and how to bring humans into the loop, whether for validation, edge case handling, or continuous improvement, and obsess over making those experiences frictionless for our users. - Ship with the customer in mind. You connect technical decisions to customer outcomes. You're energized by understanding how customers use the product, and you use that context to prioritize what to build next. You ship iteratively, measure impact, and course-correct quickly. - Collaborate across product, infrastructure, and data teams. Align technical direction with product goals, contribute to architecture decisions, and help the team move faster by establishing patterns and best practices for production-grade agentic systems. - Stay on the frontier. Evaluate and integrate advances in LLMs, retrieval architectures, and agentic reasoning. You have strong opinions (loosely held) about where this space is heading and bring that perspective to your work. WHAT YOU BRING - Experience building and operating backend systems in production, with meaningful experience in at least one of: search/retrieval, data pipelines, distributed systems, or API-heavy service architectures. - Experience building or evaluating agentic / LLM-powered systems. You've worked with retrieval-augmented generation, multi-step agent workflows, or similar architectures and have thought critically about how to evaluate their output quality at scale. - Strong software engineering fundamentals. You write clean, maintainable, well-tested code. You're comfortable with Python and have experience with backend frameworks, APIs, and production infrastructure. You care about reliability, observability, and CI/CD. - Familiarity with search and retrieval systems. You've built or significantly improved retrieval pipelines and understand information retrieval fundamentals (hybrid retrieval, relevance tuning, query understanding). You've worked with tools likePGVector, Pinecone or Elasticsearch and understand their operational tradeoffs. - Experience with cloud infrastructure (AWS, GCP, or Azure) and building systems that handle scale, large data volumes, low-latency requirements, and high availability. - You use AI coding tools to accelerate your own work. You've integrated tools like Copilot, Curssor, Claude Code, or similar into your development workflow and can speak to how they've changed the way you build software. - Customer-oriented mindset. You've shipped features where you understood the end-user problem, not just the technical specification. You're motivated by customer impact, not just technical elegance. - Ability to lead projects and provide technical direction. You can own a problem end to end, make sound architectural decisions, and help others on the team level up. NICE TO HAVE - Experience designing multi-agent systems or complex orchestration workflows. - Contributions to AI/ML open-source projects. - ML fundamentals such as precision, recall, cross-validation, bias-variance tradeoff, overfitting and regularization. - Interest in sharing learnings externally (blog posts, talks, open-source contributions). WHAT SUCCESS LOOKS LIKE We're looking for builders who: - Take ownership and run with ambiguous problems - Jump into new areas and rapidly learn what's needed to deliver solutions - Bring scientific rigor while maintaining a pragmatic delivery focus - See unclear requirements as an opportunity to shape the solution OUR TECH STACK - LLM platforms: OpenAI, Anthropic, Gemini - Agent frameworks: PydanticAI - LLMOps tools: Logfire - Search/Vector DBs: Elasticsearch, Pinecone, PostgreSQL - Infrastructure: Docker, Kubernetes, GCP, Temporal