Note: The job is a remote job and is open to candidates in USA. Laurel is on a mission to return time, transforming how organizations capture, analyze, and optimize time with their AI Time platform. They are seeking a highly experienced Applied AI Engineer to own AI features end-to-end and ensure the dependability of their core AI technology.
Responsibilities
- Own AI features end-to-end, from problem statement to GA — initiative classification, narratives, work codes, daily themes, compliance, and vision extraction (Prism)
- 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
Skills
- 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
- 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
- Take ownership over our product and the parts of it that you're responsible for. You should be invested and have a proven history of early action and initiative, rather than waiting for direction, as this is an important part of working at Laurel
- 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
Benefits
- Offers Equity
- 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
Company Overview