Live voice
Explain naturally while the interviewer probes missing assumptions and weak trade-offs.
The technical interview loop
Practice the round you are actually preparing for
A software engineer mock interview should reproduce the work and communication expected in that round, not reuse one generic chat interface for every format.
25 live scenarios
Coding and DSA mock interviews
Clarify the problem, explain your algorithm, implement in a nine-language editor, validate edge cases, and respond to follow-up constraints.
AlgorithmsData structuresComplexityTesting
Explore this track →53 live scenarios
System Design mock interviews
Move from requirements and estimates to APIs, data models, architecture, scaling, reliability, and trade-offs on a live whiteboard.
ArchitectureScalabilityReliabilityTrade-offs
Explore this track →26 live scenarios
OOD and LLD mock interviews
Turn ambiguous behavior into responsibilities, interfaces, collaborations, extensible code, and testable designs in a multi-file workspace.
OODLLDSOLIDDesign patterns
Explore this track →Focused preparation paths
Practice the exact role, level, or systems specialty
Each guide combines a timed session plan, targeted scenarios, a role-specific scorecard, FAQ answers, and automatic links back to the practice library.
Role preparation
Senior Software Engineer Mock Interview
Practice the shift from solving a contained problem to owning ambiguous requirements, technical risk, operational trade-offs, and the clarity of the team around you.
Open practice guide →Role preparation
Backend Engineer Mock Interview
Rehearse the work behind a reliable service: contract design, state transitions, concurrency control, storage choices, failure handling, and production diagnosis.
Open practice guide →Role preparation
Frontend System Design Interview
Design the browser application as a production system: user journeys, component boundaries, state ownership, network behavior, performance budgets, accessibility, and safe delivery.
Open practice guide →Role preparation
Machine Learning Engineer Mock Interview
Connect model quality to a working product: define the objective, prevent leakage, design evaluation, serve predictions, monitor drift, and make failures safe.
Open practice guide →Role preparation
Staff Engineer System Design Interview
Practice architecture at organizational scale: choose the right problem, define boundaries, manage migration and risk, and create a strategy multiple teams can execute.
Open practice guide →Frontier AI company
OpenAI Coding Interview Preparation
Build a coding practice loop around clear reasoning, correct implementation, validation, and deeper follow-ups—without relying on leaked or claimed official questions.
Open practice guide →Frontier AI company
Anthropic System Design Interview Preparation
Practice designing reliable AI products and infrastructure with explicit evaluation, safety boundaries, failure containment, observability, and human control.
Open practice guide →Frontier AI company
Google DeepMind Research Engineer Preparation
Practice the bridge between research ideas and dependable experiments: algorithms, measurement, reproducibility, scalable training, performance, and clear technical communication.
Open practice guide →Systems specialty
AI Infrastructure Interview Questions
Practice the systems behind modern AI workloads: accelerator scheduling, distributed training, inference serving, model and data movement, reliability, observability, and cost.
Open practice guide →Systems specialty
Distributed Systems Mock Interview
Practice reasoning when messages duplicate, machines fail, clocks disagree, capacity shifts, and the system still has to protect user-visible invariants.
Open practice guide →What makes the mock realistic
Your reasoning and workspace become the evidence
EngMock evaluates what you clarified, said, drew, coded, tested, and changed during the recorded session. The report does not invent a score from a final answer alone.
Purpose-built workspace
Use a whiteboard, single-file coding editor, or multi-file OOD workspace.
Interview pressure
Practice inside a 45- or 60-minute session with an explicit target level.
Evidence-based review
Replay the interview and connect feedback to the exact moments that produced it.
Top tech and frontier AI labs
Prepare for modern engineering teams, from big tech to frontier AI labs
Company guides combine current official process information with clearly labelled independent practice. They never present company-style exercises as official or leaked questions.
Big Tech
Software Engineer · Senior Software Engineer
View guide →Big Tech
Meta
Software Engineer · Production Engineer
View guide →Big Tech
Amazon
Software Development Engineer · SDE II
View guide →Big Tech
Microsoft
Software Engineer · Senior Software Engineer
View guide →Frontier AI lab
OpenAI
Software Engineer · Research Engineer
View guide →Frontier AI lab
Anthropic
Software Engineer · Member of Technical Staff
View guide →Frontier AI lab
Google DeepMind
Software Engineer · Research Engineer
View guide →Frontier AI lab
xAI
Software Engineer · Member of Technical Staff
View guide →AI engineering practice
Frontier AI systems scenarios
Design Context and Memory for a Long-Running Agent
Design context curation, compression, retrieval, and durable memory for agents operating across many hours and context windows.
View interview scenario →Design an Evaluation Platform for AI Agents
Design datasets, trace grading, simulation, regression detection, and release gates for non-deterministic tool-using agents.
View interview scenario →Design a Multi-Tenant LLM Inference Platform
Design a global inference service for mixed chat, coding, and long-context workloads across heterogeneous accelerators.
View interview scenario →Design a Fault-Tolerant Distributed LLM Training Platform
Design the training stack for a 100B+ parameter model across tens of thousands of accelerators.
View interview scenario →Design a Shared GPU Cluster Scheduler
Design scheduling and capacity management for training, research, and production inference on a large heterogeneous accelerator fleet.
View interview scenario →Design an LLM Inference Performance Optimization Program
Design the measurement, kernel, rollout, and correctness system that closes the gap between theoretical and achieved inference throughput.
View interview scenario →Run your next software engineer mock interview
Choose a technical round, set your target level, speak your reasoning aloud, and review the evidence after the session.
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