Your coding test is broken.
Sift tests what actually predicts job performance — debugging, system design, AI fluency, and product thinking. Adaptive per candidate. Impossible to cheat.
A user reports slow load times. What do you investigate first?
4,000+
Candidates interviewed about ideal assessments
92%
Said they want real-world problem solving
3x
Better signal on engineering capacity
0%
Cheat rate with adaptive assessments
Trusted by engineering teams at
What Sift Measures
Go deep on what matters.
Every assessment evaluates system design, AI fluency, and integrity in one session. Click through to see how each dimension works.
Live Architecture Canvas
Design Evaluation
Strong Decisions
Chose event-driven architecture for async workflows.
Separated read/write paths for scalability.
Identified single point of failure and added redundancy.
Design Gaps
No consideration for data consistency across services.
Missing rate limiting at service boundaries.
Dimensions Assessed
Interactive Architecture Challenges
Candidates build and iterate on system designs in real time — drawing service boundaries, choosing data stores, and defending tradeoffs as requirements evolve.
Scaling & Failure Reasoning
Assessments probe how candidates handle load spikes, failover scenarios, and bottleneck identification — the decisions that matter in production.
Beyond Whiteboard Diagrams
Sift captures the reasoning process, not just the final diagram. How candidates iterate, backtrack, and adapt their design reveals more than a static answer.
Adaptive Assessments
Test real engineering, not memorized answers.
Sift adapts each assessment in real time — evaluating debugging, product design, and AI fluency. Scores adjust continuously, surfacing strengths and red flags before late-stage interviews.
Candidate Signal Snapshot
Benchmark Outcome
Strengths
Strong debugging path selection under incomplete data.
Good prioritization: fixed highest-impact issue first.
Solid product judgment for tradeoff decisions.
Red Flags
Missed boundary validation in scoring logic.
Weak reasoning for risky fallback behavior.
Prompt strategy lacked iterative verification.
Candidate Comparison
Adaptive benchmark score vs. role peer cohort
Debugging They Haven't Seen Before
Candidates face fresh debugging scenarios, not recycled interview prompts. It measures how they reason through unfamiliar issues like they would on the job.
Real Product Design Thinking
Assessments include product tradeoffs, user constraints, and decision quality so you can evaluate how engineers design under realistic conditions.
Prompt Engineering in Context
See how candidates use AI prompts to investigate, iterate, and ship solutions. This reflects modern engineering workflows, not textbook exercises.
Hiring Signal Funnel
Radar Chart: Skill Shape
Compare candidate skill shape against benchmark cohort profiles to spot outlier strengths and hidden gaps before late-stage interviews.
FAQ
Common questions.
Ready to find the
right signal?
Join the companies replacing outdated assessments with Sift.