Product Engineer - Knowledge Intelligence Pod

Multiplier Holdings•San Francisco, CA

1d•Hybrid

About The Position

The Knowledge Intelligence Pod (KIP) is a vertical product team that owns knowledge-heavy product experiences end-to-end. KIP helps firms and internal pods find the right information, extract the right facts, verify the evidence behind those facts, and use that knowledge to answer client information requests, prepare accounting working papers, and review documents. The team owns both user-facing product experiences and the core services behind them: document intelligence, search and retrieval, evidence/provenance, answering systems, evaluation, and quality loops. We are looking for a Senior/Staff Engineer to help define and build the technical foundation for KIP. This is a hands-on technical leadership role spanning product engineering, systems architecture, applied AI, and correctness and reliability. You will write production code, design durable abstractions, set technical direction, mentor other engineers, and stay close enough to users to know whether the system is actually making professionals faster and more confident. Beyond building these systems, you’ll own how they perform, scale, and stay reliable once real professionals depend on them.

Requirements

8+ years building software, including Staff-level technical leadership on complex systems.
Product-minded and close to users. You judge success by whether professionals work faster and trust the output, not just whether the system returned a response, and you'll sit with tax pros and accountants to watch real work firsthand.
Strong across the stack. You move comfortably between backend systems, APIs, data models, review UIs, and production debugging.
Calm with ambiguity, high on ownership. You turn a fuzzy problem into a plan, a shipped system, and a measurable feedback loop, validating hypotheses and adapting as you learn, without waiting for perfect specs.
A strong technical communicator. You write crisp design docs, explain tradeoffs plainly, and align engineering, product, and domain experts around a path forward.

Nice To Haves

Ideally experienced with applied AI and/or financial systems and data.
You've worked deeply with LLMs, retrieval, extraction, and agents, or with the data behind financial and accounting workflows.

Responsibilities

Evolve the core data model across documents, emails, uploads, and new sources.
Harden the workflow we run today: ingest, extract, correct, approve, search.
Extend the APIs behind extracted data, provenance, corrections, and approvals.
Push abstractions so new firms onboard without bespoke work.
Own the extractors, document analytics, and review surfaces in production.
Deepen structured outputs with confidence signals, provenance, and correction paths.
Expand observability: traces, prompt/model versions, failure modes, regressions.
Own the review, correction, and answering surfaces professionals use.
Partner with tax professionals and accountants to automate their real workflows.
Turn ambiguous problems into scoped bets and shipped systems.
Set technical direction as systems scale across firms and pods.
Mentor engineers through design reviews, pairing, and hardening.
Raise the bar on testing, evals, observability, and security.
Partner with the Pod Lead to own the roadmap.