Goldman Sachs Behavioral Interview for AI Engineer (Engineering, New York)

This behavioral interview evaluates how an AI Engineer aligns with Goldman Sachs’ Business Principles and engineering culture within a highly regulated, client-first environment. Expect structured, scenario-based questions (STAR encouraged) focused on: 1) Client impact and judgment under pressure—e.g., trade-offs you made to protect clients or firm reputation during market-critical incidents or tight deadlines; 2) Risk, controls, and compliance mindset—how you navigated model governance, documentation, approvals, monitoring, and incident response for production ML systems; pushing back on requests from trading desks or stakeholders when policies, model risk, or data usage constraints applied; 3) Collaboration across functions and time zones—partnering with Strats, Global Markets engineers, Risk, Compliance, and product teams to deliver safe, reliable AI solutions; handling ambiguity, conflicting priorities, and handoffs; 4) Ownership and operational excellence—on-call habits, postmortems, rollback plans, reproducibility, data lineage, and continuous model improvement; 5) Data ethics and privacy—decisions around PII handling, consent, explainability, fairness, and bias mitigation in financial use cases; 6) Communication and influence—clear, concise updates to VPs/MDs, managing expectations, and earning trust; 7) Culture fit and motivation—Why Goldman Sachs, long-term orientation, integrity, teamwork, and discretion. Interviewers typically probe for concrete outcomes, measurable impact (latency, accuracy, risk reduction), and lessons learned from failures. Be ready with examples such as: handling a degraded model at market open, declining to use a dataset due to policy, improving a model while reducing infrastructure cost, or resolving a stakeholder conflict without compromising controls.