Founding AI Engineer

Job Description As a Founding Engineer at Fulkrum, you will be responsible for building the agentic systems that power the core operations of the company. Your role will involve turning messy human workflows into reliable and scalable infrastructure, thereby shaping how the company operates from day one. Key Responsibilities: Build expert sourcing operations including systems for outreach, qualification, onboarding, and ongoing engagement of domain experts across various sectors such as Consulting, IB, PE, Accounting, Legal, and Finance. Develop internal tooling by creating dashboards, pipelines, and integrations to enable a small team to operate with the efficiency of a larger one. Establish quality infrastructure by automating rubric evaluation, gold-standard checking, and audit trails to ensure that quality becomes a system property rather than a manual task. Design and build agentic workflows for data ops automation to automate the assignment, review, and delivery of expert-annotated datasets at scale. Conduct research by training Small Language Models on expert datasets. Qualifications Required: 25 years of software engineering experience at a fast-moving startup or in a role with broad ownership. Experience in building LLM-powered workflows, agents, or automation pipelines such as LangChain, CrewAI, or custom orchestration. Ability to solve vague problems and develop working systems with minimal guidance. Strong opinions about tooling and systems with the capability to articulate trade-offs clearly. Nice to have: Familiarity with post-training data formats such as SFT, DPO, RLHF. Prior experience in founding or early-stage startup environments. As a Founding Engineer at Fulkrum, you will be responsible for building the agentic systems that power the core operations of the company. Your role will involve turning messy human workflows into reliable and scalable infrastructure, thereby shaping how the company operates from day one. Key Responsibilities: Build expert sourcing operations including systems for outreach, qualification, onboarding, and ongoing engagement of domain experts across various sectors such as Consulting, IB, PE, Accounting, Legal, and Finance. Develop internal tooling by creating dashboards, pipelines, and integrations to enable a small team to operate with the efficiency of a larger one. Establish quality infrastructure by automating rubric evaluation, gold-standard checking, and audit trails to ensure that quality becomes a system property rather than a manual task. Design and build agentic workflows for data ops automation to automate the assignment, review, and delivery of expert-annotated datasets at scale. Conduct research by training Small Language Models on expert datasets. Qualifications Required: 25 years of software engineering experience at a fast-moving startup or in a role with broad ownership. Experience in building LLM-powered workflows, agents, or automation pipelines such as LangChain, CrewAI, or custom orchestration. Ability to solve vague problems and develop working systems with minimal guidance. Strong opinions about tooling and systems with the capability to articulate trade-offs clearly. Nice to have: Familiarity with post-training data formats such as SFT, DPO, RLHF. Prior experience in founding or early-stage startup environments.