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Multiplier Holdings is seeking a Senior/Staff Engineer to define and build the technical foundation for the Knowledge Intelligence Pod.
This hands-on leadership role spans product engineering, systems architecture, applied AI, and reliability; you will write production code, design durable abstractions, mentor engineers, and stay close to users to ensure the system makes professionals faster and more confident.
Full time
Hybrid
Reports to: Knowledge Intelligence Pod (KIP) Lead
Location: San Francisco or Singapore preferred; open to exceptional candidates with strong overlap across US, Europe, and Asia working hours.
Working Pattern : Full Time
Multiplier Holdings is a VC-backed startup that acquires and scales professional services firms with AI and automation. We operate in regulated, high-stakes verticals like tax and corporate accounting, where complex workflows and talent shortages make automation especially valuable.
Unlike traditional software vendors, we combine technology and service delivery under one roof, building in-house AI and workflow components configured per firm to boost efficiency and improve the experience for clients and staff. Learn more at multiplierholdings.com .
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.
KIP already runs extraction, retrieval, review, and answering systems in real tax workflows. As Staff Engineer, you'll own the core and scale what's working:
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.
You’ll help build the knowledge layer for AI-native professional services firms: the systems that turn messy documents, communications, and firm knowledge into structured, verifiable intelligence professionals can act on. KIP sits at the intersection of financial data and applied AI, where correctness is non-negotiable and the bar for accuracy is high.
The work is early enough that you can shape the architecture, engineering standards, and product direction, and real enough that you'll see whether staff trust what you build and whether it removes painful manual work. If you want to build applied AI systems that become dependable infrastructure for high-stakes professional work, this is it.
A high-impact Staff Engineer role shaping KIP's technical foundation, with direct access to real users and fast feedback loops.
Scope to influence architecture across extraction, retrieval, evaluation, and product surfaces.
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