AI Engineer

SquarePeg is building the intelligence layer for modern recruiting. We help hiring teams cut through noisy applicant pools by using AI to screen, enrich, explain, and rank candidates directly inside their ATS. Hiring is being reshaped by AI. Candidates are applying faster than ever, resumes are increasingly optimized or AI-generated, and recruiters are drowning in volume. SquarePeg helps companies identify real signal: who is qualified, why they are qualified, and what evidence supports the decision. We are a small, fast-moving team building practical AI systems that customers use in real hiring workflows every day. The Role We are hiring an AI Engineer to help build the core systems behind SquarePeg’s matching, scoring, enrichment, fraud detection, and recruiter-facing AI workflows. This is not a research-only role. You will work across product, engineering, and data to turn messy hiring data into reliable AI-powered features. You should be comfortable building production systems, experimenting with LLMs, evaluating outputs, and improving quality through better prompts, pipelines, structured data, and scoring logic. You will help us build AI that is useful, explainable, and trusted by hiring teams. What You’ll Do Build and improve AI workflows for resume parsing, candidate-job matching, scoring, fraud detection, and candidate enrichment. Design prompts, pipelines, evals, and structured outputs that improve accuracy, consistency, and explainability. Work with LLMs, embeddings, ontologies, and deterministic scoring logic to identify relevant skills, titles, industries, experience patterns, and non-obvious candidate fit. Create systems that explain why a candidate does or does not meet a requirement, with evidence recruiters can understand and audit. Improve data quality across resumes, job descriptions, company data, LinkedIn data, and ATS records. Collaborate with product and engineering to ship AI features into production, monitor performance, and iterate quickly. Build internal tools and evaluation frameworks to measure model quality, hallucination risk, false positives, false negatives, and edge cases. Think deeply about fairness, compliance, and transparency in AI-assisted hiring decisions. What We’re Looking For You have experience building production AI or data-heavy systems, ideally using LLMs, embeddings, retrieval, structured extraction, or classification. You are strong technically and can write clean, reliable code. You are comfortable working with ambiguous problems where there is no perfect dataset, no perfect label, and no single “right” answer. You think in systems: prompts, data pipelines, evals, product UX, edge cases, latency, cost, and reliability. You care about explainability. You do not treat AI as magic, and you want users to understand why a system produced a result. You are pragmatic. You know when to use an LLM, when to use deterministic logic, and when to combine both. You can move quickly without being sloppy. You are excited about applying AI to a real-world workflow where accuracy, trust, and usability matter. Nice to Have Experience with recruiting, HR tech, marketplaces, search, ranking, matching, or recommendation systems. Experience building eval frameworks for LLM applications. Experience with structured data extraction from messy documents. Experience with Python, Node.js , TypeScript, React, PostgreSQL, embeddings, vector search, or modern LLM APIs. Experience with compliance-sensitive AI systems, explainability, bias testing, or auditability. Why Join Us You will work on a real AI product with immediate customer impact. You will have ownership over core AI systems, not just small feature tickets. You will help define how AI should be used responsibly in hiring. You will join early enough to shape the product, architecture, and company. You will work with a small team that values speed, clarity, and high-quality execution.