What you’ll be doing

•    Building the AI systems behind our product: research agents, RAG over large catalogs, structured extraction from messy sources (PDFs, spec sheets, safety data sheets), and entity resolution.

•    Treating AI as a measurement problem: we run curated eval sets on every PR and trace every agent call. You’ll build evals and golden datasets, define metrics, run experiments, and ensure changes actually improve the product.

•    Pushing past brute-force LLM calls: the naive approach (an LLM call for every problem) gets slow, costly, and inconsistent fast. Some of our most interesting work is what comes next.

•    Working with data at scale: building pipelines that ingest, normalize, and enrich catalogs of tens of thousands to millions of SKUs, with care for correctness and cost.

•    Owning features end-to-end: shaping the idea, building an MVP, testing in production, and iterating. Full-stack by default, with frontend and design support when you need it.

•    Talking with and supporting customers: joining customer calls to ask questions and share early work, plus rotating into support to solve issues (some on-call time is part of the role).

Requirements

•    Strong software engineering fundamentals: you take a system from idea to deployed and stable.

•    A data science or applied-ML background: comfortable with experimentation, evaluation, and reasoning about data.

•    Experience building information retrieval, search, recommendation, or extraction systems (embeddings, ranking, RAG, or similar).

•    Comfort with backend and data engineering: APIs, databases, and data pipelines (TypeScript, Postgres, and BigQuery are our stack).

•    Comfort operating in ambiguity and taking initiative without perfect specs.

•    Strong writing skills: you can make a complex idea clear in a few sentences.

•    Must be legally authorized to work in the US. We are unable to provide sponsorship for this role.

Nice to have

•    Shipped LLM-powered features in production, including the eval and observability work that makes them trustworthy.

•    Experience with big data tooling (BigQuery, warehouses, large-scale ETL).

•    A graduate background or research experience in ML, IR, NLP, or a quantitative field, paired with a track record of shipping.

•    Experience with code-generation pipelines, classical ML, or turning probabilistic systems into deterministic ones.

•    Experience at an early-stage SaaS startup or working closely with customers in a technical context.