Engineering Assessments: The Complete Guide

Why Engineering Assessments Matter

The Problem with Resumes Alone

Resumes tell you what candidates claim they've done, not what they can actually do. The gap between resume claims and actual ability is significant:

Resumes are marketing documents, not technical evaluations. Assessments provide evidence of actual ability.

What Assessments Should Predict

Effective assessments answer these questions:

Technical competence:

• Can they write working code?
• Do they understand fundamental concepts?
• Can they debug problems systematically?
• Do they know when to ask for help vs. persist?

Problem-solving approach:

• How do they break down complex problems?
• Do they consider edge cases?
• Can they communicate their thinking?
• Do they make reasonable trade-offs?

Job-relevant skills:

• Can they do the actual work they'd do in the role?
• Do they understand the domain (if relevant)?
• Can they work with the tools/stack (if required)?
• Do they demonstrate the level expected?

Collaboration and communication:

• Can they explain technical concepts?
• Do they ask clarifying questions?
• Can they receive feedback constructively?
• Do they work well with others (in pair programming formats)?

Assessment Formats: Pros, Cons, and When to Use

Take-Home Projects

What it is: Candidates receive a problem statement and work independently, typically with 2-7 days to complete it. They submit code, documentation, and sometimes a brief explanation.

Best for:

• Evaluating ability to work independently
• Assessing code quality, testing, documentation
• Roles where candidates work on larger features/projects
• Candidates who perform better without time pressure

Pros:

• Respects candidate time (they choose when to work)
• Shows code quality and engineering practices
• Mimics real work more closely
• Less stressful than live coding
• Can evaluate multiple dimensions (code, tests, docs, architecture)

Cons:

• Can't observe problem-solving process
• Risk of candidates getting help or using solutions online
• Takes longer to evaluate (reviewing code vs. observing)
• May not reflect how they work under time pressure
• Candidates may invest more time than intended

Design principles:

• Time-boxed: Clearly state expected time (2-4 hours max)
• Realistic: Problem should mirror actual work
• Scoped: Small enough to complete, large enough to assess
• Clear criteria: Share evaluation rubric upfront
• No tricks: Test ability, not puzzle-solving

Example good take-home:
"Build a simple API endpoint that accepts a GitHub username and returns their public repositories sorted by stars. Include tests, error handling, and a README explaining your approach. We expect this to take 2-3 hours."

Example bad take-home:
"Build a full-stack application with authentication, real-time updates, and deployment. Use any tech stack. No time limit." (Too vague, too large, no boundaries)

Live Coding Sessions

What it is: Candidate and interviewer work together on a coding problem in real-time, typically using a shared editor (CoderPad, CodePen, or similar). Interviewer observes problem-solving process, asks questions, and may provide hints.

Best for:

• Evaluating problem-solving approach under pressure
• Assessing communication and collaboration
• Roles where candidates need to code in real-time (pair programming, debugging sessions)
• Fast-moving hiring processes

Pros:

• Observes thinking process in real-time
• Can assess communication and collaboration
• Faster evaluation (immediate feedback)
• Harder to game (can't look up solutions)
• Shows how candidates handle hints and feedback

Cons:

• High stress can affect performance
• May not reflect how they work independently
• Time pressure can mask deeper understanding
• Requires skilled interviewers (knowing when to help vs. observe)
• Can disadvantage candidates who need time to think

Design principles:

• Appropriate difficulty: Should be solvable in 30-45 minutes
• Multiple approaches: Problem should have several valid solutions
• Progressive hints: Interviewer can guide without solving
• Focus on process: Solution matters less than approach
• Clear communication: Explain what you're evaluating

Common mistakes:

• Using problems candidates can memorize (LeetCode hard)
• Focusing only on whether they solve it (not how)
• Not providing any hints when stuck
• Judging syntax errors harshly
• Comparing to optimal solution instead of acceptable ones

System Design Assessments

What it is: Candidate designs a system (e.g., "design a URL shortener" or "design a chat system") by discussing requirements, drawing diagrams, discussing trade-offs, and considering scale. Typically 45-60 minutes.

Best for:

• Senior+ roles where architecture matters
• Evaluating ability to think at scale
• Assessing trade-off reasoning
• Roles involving distributed systems

Pros:

• Evaluates high-level thinking
• Shows how candidates approach ambiguity
• Assesses communication of complex ideas
• Relevant for senior roles
• Less about memorization, more about reasoning

Cons:

• Requires experienced interviewers
• Can be abstract (harder to evaluate objectively)
• May not be relevant for junior/mid roles
• Time-consuming to conduct well
• Easy to make too hard or too easy

Design principles:

• Start broad: Begin with high-level design, then drill down
• Requirement gathering: Evaluate how they ask clarifying questions
• Multiple valid approaches: Don't have one "right" answer
• Scale discussion: Include questions about scaling (but don't require perfect answers)
• Trade-off focus: What matters is reasoning, not perfect solutions

Example progression:

1. Requirements (5 min): "What are the core features? What's the scale?"
2. High-level design (15 min): "Draw the major components and data flow"
3. Deep dive (20 min): "How would you handle [specific challenge]?"
4. Scale discussion (10 min): "How does this change at 10x, 100x scale?"
5. Trade-offs (10 min): "What are the downsides of your approach?"

Portfolio Reviews

What it is: Evaluation of candidate's past work—GitHub repositories, deployed projects, technical blog posts, open-source contributions, or code samples they provide.

Best for:

• Candidates with public work (open source, side projects)
• Evaluating code quality and engineering practices
• Assessing domain expertise (if portfolio is relevant)
• Roles where past work is strong signal

Pros:

• Shows actual work quality (not just claims)
• Respects candidate time (no extra work)
• Can evaluate multiple projects
• Demonstrates consistency over time
• Shows initiative (if work is self-directed)

Cons:

• Not all candidates have portfolios
• May not reflect current ability (old code)
• Hard to standardize evaluation
• Can't observe problem-solving process
• May not be relevant to role (side project vs. work project)

Design principles:

• Ask for specific examples: "Share 2-3 projects that best represent your work"
• Evaluate holistically: Code quality, tests, documentation, architecture
• Consider context: Side project vs. work project vs. open source
• Look for growth: Does code quality improve over time?
• Ask questions: "Walk me through how you approached [specific part]"

Designing Effective Assessments

Principle 1: Mirror Real Work

Assessments should resemble what candidates would actually do in the role. If the role involves building APIs, assess API design. If it involves debugging production issues, assess debugging. If it involves code review, assess code review.

Good: Backend role → "Design and implement a REST API endpoint"
Bad: Backend role → "Solve this graph algorithm problem"

Why it matters: Candidates who can solve abstract problems may not be able to do the actual job. Assessments that mirror work predict performance better.

Principle 2: Respect Candidate Time

Candidates are evaluating you too. Assessments that take 8+ hours signal that you don't value their time. Top candidates will decline.

Time guidelines:

• Take-home projects: 2-4 hours maximum
• Live coding: 45-90 minutes
• System design: 45-60 minutes
• Portfolio review: 30-60 minutes (their time to prepare, your time to review)

If you need more signal:

• Use multiple shorter assessments (not one long one)
• Make later assessments conditional (only if they pass earlier ones)
• Compensate candidates for significant time investment (4+ hours)

Principle 3: Provide Clear Evaluation Criteria

Candidates should know what you're evaluating. Share rubrics upfront:

Example rubric for take-home:

• Functionality (30%): Does it work? Handles edge cases?
• Code Quality (25%): Readable, maintainable, follows best practices?
• Testing (20%): Adequate test coverage? Tests are meaningful?
• Documentation (15%): README explains approach? Code is commented appropriately?
• Architecture (10%): Well-structured? Appropriate abstractions?

Benefits:

• Candidates know what to focus on
• Reduces anxiety (clear expectations)
• Enables self-assessment
• Makes evaluation more objective

Principle 4: Focus on Process Over Perfection

Perfect solutions are less important than good problem-solving. Evaluate:

• How they approach the problem
• How they handle obstacles
• How they communicate their thinking
• How they make trade-offs
• How they handle feedback

In live coding: A candidate who gets stuck but asks good questions and works through it systematically may be stronger than one who solves it quickly but can't explain their approach.

In take-homes: Code that works but has some rough edges may be better than perfect code that took 20 hours (signals they don't respect boundaries).

Principle 5: Make It Collaborative (When Possible)

Assessments shouldn't feel like exams. In live formats, make it collaborative:

• "Let's work through this together"
• "What are you thinking?"
• "Here's a hint if you want it"
• "How would you approach this?"

This evaluates collaboration (important for most roles) and reduces stress (better signal).

Common Assessment Mistakes

Mistake 1: Assessments That Are Too Hard

Problem: Using assessments designed to filter out 90% of candidates. This filters out qualified candidates too.

Why it happens: Interviewers want to feel like they're maintaining high standards. Hard problems feel rigorous.

Why it's wrong: If 90% of qualified candidates fail your assessment, the assessment is broken, not the candidates. You're optimizing for false negatives (rejecting good candidates) over false positives (accepting weak candidates).

Solution: Design assessments where 60-70% of qualified candidates should pass. If everyone passes, make it slightly harder. If almost no one passes, make it easier.

Mistake 2: Unclear or Vague Instructions

Problem: "Build something interesting" or "Solve this problem" without context, constraints, or evaluation criteria.

Why it happens: Interviewers assume candidates will figure it out or want to see how they handle ambiguity.

Why it's wrong: Ambiguity in assessments tests ability to read minds, not technical skills. Different candidates interpret instructions differently, making evaluation unfair.

Solution: Provide clear problem statements, constraints, expected outputs, and evaluation criteria. If you want to assess ambiguity handling, do that explicitly in a separate conversation.

Mistake 3: Assessments That Don't Match the Role

Problem: Using the same assessment for all engineering roles, or using assessments that test skills irrelevant to the role.

Why it happens: Easier to reuse assessments than create role-specific ones.

Why it's wrong: Frontend engineers don't need to solve backend scaling problems. Backend engineers don't need to build pixel-perfect UIs. Misaligned assessments predict performance poorly.

Solution: Create role-specific assessments. If you must reuse, adapt them (e.g., same problem, different focus—frontend focuses on UI, backend focuses on API).

Mistake 4: No Feedback or Follow-Up

Problem: Candidates complete assessments but receive no feedback, or feedback is generic ("thanks for your time").

Why it happens: Time constraints, or assumption that rejected candidates don't need feedback.

Why it's wrong: Candidates invested time. They deserve to know what they did well and what they could improve. This affects your employer brand.

Solution: Provide specific feedback:

• "Your code was clean and well-tested. The architecture was solid. We were looking for more consideration of edge cases in the error handling."
• Even for rejections, 2-3 sentences of feedback shows respect.

Mistake 5: Using Assessments Too Early or Too Late

Problem: Either requiring assessments before candidates know if they're interested, or only after multiple interviews when they've invested significant time.

Why it happens: Different philosophies on when to assess—some want to filter early, others want to build rapport first.

Why it's wrong: Too early feels like a gatekeeper (candidates may not be invested yet). Too late wastes everyone's time if there's a fundamental mismatch.

Solution: Use assessments after initial screening (they're interested, you're interested) but before deep cultural/team fit interviews. Typically: recruiter screen → assessment → technical deep dive → team fit.

Evaluating Assessment Results

What to Look For

Strong signals:

• Code that works and handles edge cases
• Clear problem-solving approach
• Good communication of thinking
• Appropriate trade-offs for the context
• Questions that show understanding
• Code quality that matches role level

Concerning signals:

• Code that doesn't work (and no explanation of blockers)
• Can't explain their approach
• No consideration of edge cases or errors
• Over-engineered or under-engineered for the problem
• Doesn't ask clarifying questions when needed
• Code quality significantly below role level

Red flags:

• Solution appears copied (style doesn't match their communication)
• Can't explain basic decisions
• Gets defensive about feedback
• Doesn't follow instructions (ignores constraints)
• No tests when testing was requested

Avoiding Bias in Evaluation

Common biases:

• Similarity bias: Favoring candidates who approach problems like you do
• Halo effect: Overweighting one strong aspect (e.g., clean code) and ignoring weaknesses
• Confirmation bias: Looking for evidence that confirms initial impression
• Anchoring: Being influenced by first impression or one interviewer's opinion

Mitigation strategies:

• Use rubrics with specific criteria
• Evaluate independently before discussing
• Focus on evidence, not feelings
• Calibrate with other interviewers
• Review assessments blind (without knowing other signals)

When Assessments Disagree with Other Signals

Sometimes assessment results conflict with resume, interviews, or references. How to reconcile:

Assessment weak, other signals strong:

• Was the assessment appropriate for their background?
• Did they understand the problem?
• Were there external factors (stress, technical issues)?
• Consider: Can they do the job despite weak assessment? (Maybe assessment was misaligned)

Assessment strong, other signals weak:

• Is their experience actually relevant?
• Do they have the soft skills needed?
• Can they work independently? (Take-homes don't test this)
• Consider: Strong assessment may indicate potential, but gaps elsewhere may be blockers

General principle: Assessments are one signal among many. Don't let one assessment override everything else, but don't ignore clear assessment signals either.

Building an Assessment Process

Step 1: Define What You're Assessing

Before creating assessments, clarify:

• What skills are required for this role?
• What level of proficiency is expected?
• What would success look like in this assessment?
• What would failure look like?

Example for mid-level backend engineer:

• Can write working API endpoints
• Understands basic database concepts
• Can write reasonable tests
• Can explain their approach
• Handles errors appropriately
• Code is readable and maintainable

Step 2: Choose Assessment Format(s)

Based on what you're assessing, choose format(s):

• Take-home for code quality and independent work
• Live coding for problem-solving process
• System design for architecture thinking
• Portfolio for past work quality

You may use multiple formats (e.g., take-home + live discussion about their solution).

Step 3: Design the Assessment

Create the problem/exercise:

• Mirrors real work
• Appropriate scope (2-4 hours for take-home)
• Clear instructions and constraints
• Multiple valid approaches
• Evaluation rubric

Test it internally: Have current engineers complete it. Does it take the expected time? Is it appropriately difficult? Are evaluation criteria clear?

Step 4: Train Evaluators

Interviewers need training:

• How to evaluate against rubrics
• What to look for (and what not to)
• How to provide feedback
• How to avoid bias
• When to give hints (in live coding)

Step 5: Implement and Iterate

Roll out assessments:

• Collect feedback from candidates
• Track pass/fail rates (are they reasonable?)
• Correlate assessment results with job performance (do they predict success?)
• Iterate based on data

Key metrics:

• Assessment completion rate (do candidates finish?)
• Time investment (actual vs. expected)
• Pass rate (should be 60-70% for qualified candidates)
• Correlation with job performance (do assessment scores predict success?)

Special Considerations

Remote Assessments

Remote assessments require additional considerations:

Technical setup:

• Provide clear instructions for environment setup
• Test the platform yourself (CoderPad, etc.)
• Have backup plans if technology fails
• Ensure candidates have required tools/access

Fairness:

• Not all candidates have ideal setups (quiet space, good internet)
• Consider offering alternatives (take-home if live coding has issues)
• Don't penalize technical difficulties outside their control

Security:

• Accept that candidates may look things up (focus on process, not memorization)
• For take-homes, use problems that can't be easily copied
• Focus on evaluation that can't be gamed (explanation, trade-offs, reasoning)

Accommodations

Some candidates need accommodations:

• Extra time (for candidates with disabilities)
• Different format (take-home instead of live coding for anxiety)
• Assistive technology support
• Clearer instructions or examples

Legal requirement: In many jurisdictions, you must provide reasonable accommodations. Even if not legally required, it's the right thing to do.

How to handle:

• Ask candidates if they need accommodations (don't wait for them to ask)
• Provide accommodations without judgment
• Don't treat accommodated assessments differently in evaluation

Compensating Candidates

For significant time investments (4+ hours), consider compensation:

• Paid take-home projects
• Gift cards or swag
• Donation to charity of their choice

Why it matters: Shows you value their time. Signals that you're serious about hiring (not just collecting free work).

When to compensate:

• Take-homes over 4 hours
• Multiple assessments
• Custom assessments (not standard)
• Final-stage assessments (they've invested significant time already)

Conclusion

Engineering assessments are powerful tools for evaluating technical ability, but only when designed thoughtfully. Effective assessments mirror real work, respect candidate time, provide clear criteria, focus on process over perfection, and are evaluated fairly.

The goal isn't to filter out as many candidates as possible—it's to accurately predict who will succeed in the role. Assessments that are too hard, too vague, or too disconnected from the job fail at this goal, even if they feel rigorous.

Invest in designing good assessments. Train evaluators. Iterate based on feedback and data. The upfront investment pays off in better hiring decisions and better candidate experience—both critical for building great engineering teams.