Expert-crafted business-domain datasets
for AI labs and agents

Fulkrum is building a network of domain experts to craft post-training data and evals for AI copilots and agents. We're looking for a Founding Engineer to build the agentic systems that power our core operations - turning messy human workflows into reliable, scalable infrastructure. You'll move fast, own outcomes end-to-end, and shape how the company operates from day one.

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  Data ops automation - design and build agentic workflows that automate how we assign, review, and deliver expert-annotated datasets at scale.
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  Expert sourcing ops - build systems for outreach, qualification, onboarding, and ongoing engagement of domain experts across Consulting, IB, PE, Accounting, Legal, and Finance.
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  Internal tooling - create the dashboards, pipelines, and integrations that let a small team operate with the leverage of a much larger one.
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  Quality infrastructure - automate rubric evaluation, gold-standard checking, and audit trails so quality is a system property, not a manual task.

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  Have 2–5 years of software engineering experience at a fast-moving startup or in a role with broad ownership.
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  Have built LLM-powered workflows, agents, or automation pipelines (LangChain, CrewAI, custom orchestration, etc.).
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  Are comfortable going from a vague problem to a working system with minimal guidance.
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  Have strong opinions about tooling and systems, and can articulate trade-offs clearly.

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  Experience with data annotation platforms or labeling infrastructure.
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  Familiarity with post-training data formats (SFT, DPO, RLHF).
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  Prior founding or early-stage startup experience.

Fulkrum is building a network of seasoned professionals to create training data and evaluations that teach AI models how to think like domain experts. As a Management Consulting Expert, you'll draw on your real-world experience to craft high-quality examples, evaluate AI responses, and design complex scenarios that reflect the nuance of actual consulting work.

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  Write expert-level responses to consulting scenarios - strategy, org design, market entry, operational improvement, M&A due diligence, and more.
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  Evaluate and rank AI-generated outputs against structured rubrics, identifying where models fall short of consultant-grade thinking.
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  Design realistic, complex problem sets drawn from your professional experience - the kind junior analysts routinely get wrong.
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  Review and provide detailed feedback on datasets created by other contributors.

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  2+ years at a top-tier consulting firm (MBB, Big 4 strategy, or boutique strategy) or equivalent in-house strategy role.
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  Strong written communication - you can explain complex reasoning clearly and concisely.
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  Comfort working with structured rubrics and evaluation criteria.
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  Genuine interest in AI and how large language models are trained.

We're looking for investment banking professionals to help train AI models on the real craft of banking - financial modelling, deal structuring, pitch preparation, and credit analysis. Your expertise will directly shape how AI copilots understand and reason about complex financial transactions.

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  Write detailed, expert-level responses to IB scenarios - DCF and LBO modelling logic, deal structuring, valuation rationale, pitch narrative, and more.
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  Evaluate and rank AI-generated financial analyses for accuracy, reasoning quality, and professional standard.
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  Create realistic deal scenarios, data rooms, and edge cases drawn from your transaction experience.
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  Flag errors in financial reasoning that a trained analyst would catch but a generalist model wouldn't.

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  2+ years in investment banking (bulge bracket, elite boutique, or mid-market), leveraged finance, or capital markets.
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  Deep familiarity with financial modelling, valuation methodologies, and transaction documentation.
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  Strong written communication - able to articulate complex financial logic clearly and precisely.
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  High bar for technical accuracy and attention to detail.

We're hiring private equity professionals to help build training data that teaches AI models how to think like experienced investors and operators. From deal sourcing and underwriting to portfolio management and exit strategy - your hands-on experience is exactly what's missing from today's AI models.

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  Write expert-level responses to PE scenarios - investment theses, buy-side due diligence, operating model analysis, value creation planning, and exit structuring.
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  Evaluate and rank AI outputs on deal memos, financial models, and portfolio analysis against structured quality rubrics.
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  Create realistic investment scenarios, management presentations, and IC memo examples from your deal experience.
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  Identify nuanced errors in PE reasoning that a second-year associate would catch but a model trained on public data wouldn't.

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  2+ years in private equity, growth equity, venture capital, or a related investing role.
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  Experience with the full deal lifecycle - from sourcing and underwriting to board engagement and exit.
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  Strong analytical writing and ability to construct and critique investment theses.
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  Genuine interest in applied AI and how models learn from expert knowledge.

Fulkrum is looking for accounting professionals to help train AI models on real-world financial reporting and accounting work. The gap between how AI handles accounting tasks and how a qualified accountant does is significant - your expertise helps close it, making AI copilots genuinely useful for finance teams.

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  Write authoritative responses to accounting scenarios - revenue recognition, consolidation, lease accounting, tax provision, financial statement analysis, and audit procedures.
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  Evaluate AI-generated accounting outputs for technical accuracy, appropriate standards application (GAAP/IFRS), and professional quality.
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  Design complex, realistic accounting problems and edge cases - multi-entity structures, grey-area judgements, and disclosure questions.
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  Review and quality-check datasets produced by other accounting contributors.

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  3+ years of professional accounting experience in audit, advisory, FP&A, controllership, or tax.
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  A relevant qualification or progress towards one (CPA, ACA, ACCA, CMA, or equivalent).
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  Deep familiarity with US GAAP and/or IFRS and how standards apply in practice.
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  Strong written communication - able to explain complex accounting treatments clearly and precisely.

Fulkrum is looking for legal professionals to help train AI models on the real craft of legal reasoning and drafting. The gap between how AI handles legal tasks and how a qualified lawyer does is significant - your expertise helps close it, making AI copilots genuinely useful for legal teams and in-house counsel.

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  Write authoritative responses to legal scenarios - contract analysis, M&A documentation, regulatory interpretation, due diligence, and litigation strategy.
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  Evaluate AI-generated legal outputs for accuracy, appropriate legal reasoning, and professional quality.
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  Design complex, realistic legal problems and edge cases - multi-party transactions, jurisdictional nuances, and grey-area judgment calls.
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  Review and quality-check datasets produced by other legal contributors.

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  3+ years of experience in corporate law, M&A, finance, litigation, or regulatory practice at a top-tier firm or in-house.
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  A law degree and qualification to practice (bar admission, solicitor qualification, or equivalent).
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  Strong written communication - able to explain complex legal reasoning clearly and precisely.
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  Genuine interest in AI and how models learn from expert legal knowledge.

AI copilots for Consulting, IB, and PE are being built right now - and the people training them are mostly generic crowdworkers who've never seen a deal memo. We're fixing that. Fulkrum builds expert-crafted training data that teaches AI models how to actually think like practitioners. As a pre-MBA intern, you'll sit at the intersection of frontier AI and the business domains you know cold - shaping how the next generation of AI agents reason about real professional work.

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  Design AI training scenarios - translate your deal and client experience into the prompts, rubrics, and edge cases that teach AI agents how to reason through real business problems.
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  Evaluate AI agent outputs - work hands-on with LLM outputs and agentic workflows, scoring responses and identifying where models fail on business tasks.
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  Build agentic workflows - work alongside the founding team to automate how we assign, review, and deliver expert-annotated data using AI tools and no-code automation.
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  Source and manage domain experts - identify and onboard practitioners from top Consulting, IB, and PE firms to contribute to our expert network.
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  Client and ops support - scope incoming projects, track deliverables, and keep operations tight as we scale.

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  Hands-on experience building and evaluating AI agents - a genuine differentiator for MBA applications and post-MBA roles in AI strategy, product, or investing.
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  Deep fluency in how frontier AI models are trained - RLHF, SFT, agent evaluation, reward modelling - explained in terms that make sense to a banker or consultant.
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  A front-row seat at an early-stage AI company, working directly with the founding team from day one.

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  1-3 years at a top-tier Consulting, IB, or PE firm - you know what good work looks like in these domains.
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  Strong written communication - you can structure complex ideas clearly and precisely.
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  Genuine excitement about AI - you've been playing with LLMs, reading about agents, and thinking about where this is going.
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  Comfort with ambiguity and a bias for action in fast-moving environments.