Systems Architecture & Distributed Systems Topics
Large-scale distributed system design, service architecture, microservices patterns, global distribution strategies, scalability, and fault tolerance at the service/application layer. Covers microservices decomposition, caching strategies, API design, eventual consistency, multi-region systems, and architectural resilience patterns. Excludes storage and database optimization (see Database Engineering & Data Systems), data pipeline infrastructure (see Data Engineering & Analytics Infrastructure), and infrastructure platform design (see Cloud & Infrastructure).
Systems Thinking and Interdependencies
Understanding and reasoning about how decisions and changes in one part of a product, system, or organization affect other parts. This includes mapping technical, organizational, market, and user behavior dependencies; identifying feedback loops and cascading effects; anticipating unintended consequences; evaluating trade offs between local optimizations and global outcomes; designing for resilience, observability, and graceful degradation; and using diagrams, dependency graphs, and metrics to communicate systemic impacts. Interviewers assess the candidate for the ability to reason across boundaries, prioritize cross system trade offs, surface hidden coupling, and propose solutions that optimize overall system health rather than only isolated components.
Addressing Real World Company Challenges
Practice diagnosing and proposing practical, evidence based solutions for problems organizations face in production environments and research programs. This includes analyzing public information about a target company to identify likely infrastructure bottlenecks such as scaling limits, deployment complexity, observability gaps, technical debt, and single points of failure, and outlining concrete remediation plans with trade off analysis, rollout strategies, monitoring and validation metrics. It also covers real world research challenges such as difficulty recruiting target participants, unexpected or contradictory findings, lack of stakeholder buy in, tooling or data quality issues, and compressed timelines; candidates should explain troubleshooting steps, adaptations to study design, stakeholder alignment strategies, ethical and privacy considerations, and how to measure impact. Good answers demonstrate structured problem solving, risk assessment, prioritization, cross functional communication, incremental implementation plans, and how success would be validated and iterated on.
Decision Making Under Uncertainty
Focuses on frameworks, heuristics, and judgment used to make timely, defensible choices when information is incomplete, conflicting, or evolving. Topics include diagnosing unknowns, defining decision criteria, weighing probabilities and impacts, expected value and cost benefit thinking, setting contingency and rollback triggers, risk tolerance and mitigation, and communicating uncertainty to stakeholders. This area also covers when to prototype or run experiments versus making an operational decision, how to escalate appropriately, trade off analysis under time pressure, and the ways senior candidates incorporate strategic considerations and organizational constraints into choices.