Applied AI Engineer

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🔥 5 minutes ago

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Laurel

51 - 200 employees

Founded 2020

⚡ Productivity

☁️ SaaS

Productivity • SaaS

Laurel is a company that offers a digital timekeeping solution, enabling users to manage and track their time efficiently. The platform provides a streamlined timekeeping experience through an easy-to-use interface, accessible via signing into their service.

📋 Description

• Own AI features end-to-end, from problem statement to GA • Move features through our rollout tiers (prototype → design partner → beta → GA), earning each promotion with an eval baseline and a capacity plan • Build and defend evals: golden datasets, a north-star metric per feature, and online monitoring — so we can *see and prove* how a feature behaves, not guess • Lead the "meaning" layer of our semantic stack: retrieval semantics, embedding quality (proven first on initiative-candidate retrieval), and the label flywheel that feeds prompt optimization and golden sets. You co-design our vector and knowledge-graph stores with the AI Platform team — you're their most demanding customer • Push output quality with the levers that matter: context assembly, prompt optimization, and per-customer/per-firm generation • Work closely with cross-functional teams — product managers, designers, and other engineers — to gather requirements, define measures of success, and deliver high-quality solutions • We want you not just to build but to *engage* with our product and our users. Your input and opinion will be highly valuable, and it is expected that you have many thoughts on how things should be done and why

🎯 Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience • 7+ years of professional software engineering experience, including a track record of shipping ML/LLM-powered features to production and owning their quality • You **think in problems, not solutions** — you can take a half-formed prototype and find the real customer problem behind it • Have *deep knowledge and understanding*, as well as *strongly-held opinions* about some of the below areas (or similar). We want to hear your thoughts on best practices, patterns, failure modes, and how you *consistently* ship AI features of the *highest* quality. What would you teach our other engineers? What has bitten you in the past? • **LLM application engineering:** prompting, structured outputs, retrieval/context assembly (RAG), agentic workflows • **Evaluation:** golden datasets, offline harnesses and online monitoring, north-star metrics — measuring quality rather than eyeballing it • **Retrieval & embeddings:** vector search, semantic retrieval quality, and the data/label flywheel that improves it • **Languages & stack:** strong in Python and/or TypeScript; comfortable across a modern backend (Node.js / NestJS, MongoDB, message queues) — nice to have given our stack • You use **AI coding tools** fluently in your daily work (Claude Code, Cursor, Copilot, agentic coding) and have a point of view on where they accelerate real engineering and where they don't • Strong analytical and problem-solving skills, with the ability to think critically and creatively. Ability to work in a fast-paced, dynamic environment and manage multiple priorities • Excellent communication and collaboration skills, with the ability to work effectively in a team-oriented environment • A customer mindset with proven ability to engage with customers and understand their needs.

🏖️ Benefits

• Great employee benefits, including equity and 401K • Bi-annual, in-person company off-sites, in unique locations, to grow and share time with the team • An opportunity to perform at your best while growing, making a meaningful impact on the company's trajectory, and embodying our core values: understanding your "why," dancing in the rain, being your whole self, and sanctifying time

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