Forward DeployedExpert-built kit

Forward Deployed Product Designer

Observes frontline work, maps workflows and handoffs, and turns findings into tested workflow opportunities.

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Interview content for Junior

40
What to ask. Competency and attitude questions, assigned to the right round.
261
What to listen for. Positive and negative indicators, per question.
12
What the hire must do. Capabilities with expected proficiency at each level.

Look inside: one question, as it appears in the kit

Pick the level you’re hiring. The sample changes with the level you select.

Round 3 · Peer Technical — Prototyping and Delivery Pairing21 competency questions

Delivery Partnership and Responsible Practice

AI-assisted design and responsible verification

Checks approved AI processing and retention rules before using AI assistance, generates alternatives compared against observed tasks, and manually verifies outputs before user testing.

Expected at Junior

Sample competency question

Share an experience when you used AI tools to explore alternatives. How did you check the outputs before showing them to users?

Ask once, as written, then allow silence. A helpful rephrase may hand the candidate the answer.

Positive indicators

  • States the data rules checked first
  • Shows manual verification of outputs
  • Tests AI behavior with representative users

Negative indicators

  • Pastes sensitive data into unapproved tools
  • Ships AI output without verification
  • No fallback when suggestions fail

Bar 2: routine participation with guidance or within prescribed patterns; independent ownership of this competency belongs to the next level.

Ryan Mahoney

Why this role is hard · Ryan Mahoney

Hiring at this level is hard because the work looks simple from the outside and falls apart in the details. You are judging whether someone can stand beside a busy frontline worker, listen carefully enough to capture what actually happens, and explain what they saw so plainly that engineers can build it. The best evidence is small and concrete: notes that record the interruption that broke the flow, a prototype that covers the stale and offline states, labels tested with a nervous first-time user. Polished portfolios hide this. You need the person who asks one more question about the workaround scribbled in the margin.

Everything in the download, in the order you’ll use it

Level guides for Junior, Mid and Senior.

Before you post

  • 1Ready-to-use job description
  • 3Video screening prompts
  • 8Resume screening criteria

In the room

  • 21Competency interview questions
  • 19Attitude interview questions
  • 1Hands-on work simulations
  • 1Presentation prompts

At the debrief

  • Progression framework
  • Exceeds / Meets / Below anchors for every exercise
  • 4Interview plan with time per round

Core Evaluation

Critical questions for this role

The competency and attitude questions below are where the hiring decision is made. They run in the live interview rounds and are calibrated to the level selected above.

21 Competency Questions

1 of 21

  1. Discipline

    Delivery Partnership and Responsible Practice

  2. Job requirement

    AI-assisted design and responsible verification

    Checks approved AI processing and retention rules before using AI assistance, generates alternatives compared against observed tasks, and manually verifies outputs before user testing.

  3. Expected at Junior

    Bar 2: routine participation with guidance or within prescribed patterns; independent ownership of this competency belongs to the next level.

Interview round: Peer Technical — Prototyping and Delivery Pairing

Share an experience when you used AI tools to explore alternatives. How did you check the outputs before showing them to users?

Positive indicators

  • States the data rules checked first
  • Shows manual verification of outputs
  • Tests AI behavior with representative users

Negative indicators

  • Pastes sensitive data into unapproved tools
  • Ships AI output without verification
  • No fallback when suggestions fail

19 Attitude Questions

1 of 19

Accountability Mindset

Owning misses openly and driving correction through to verified resolution.

Interview round: Peer Technical — Prototyping and Delivery Pairing

Describe a situation where a miss in your work surfaced after testing or release. What did you do? Focus on one bounded workflow.

Positive indicators

  • Acknowledges own errors and missed signals without minimizing or deflecting
  • Reconstructs what went wrong with users or support before assigning causes
  • Separates interface defects, training gaps, and policy constraints honestly in issue records
  • Follows corrections through to retesting and recorded improvement
  • Reports residual errors and limits on causal attribution after fixes ship

Negative indicators

  • Rushing through conversations

Supporting Evaluation

How candidates earn the selection conversation

The goal is to reduce effort for everyone by collecting more useful signals before adding more interviews. Lightweight application prompts and structured screens help your team focus interview time on the candidates most likely to succeed.

Stage 1 · Application

Filter at the door

Runs the moment a candidate hits Submit. Disqualifying answers end the application; everything else is captured for review.

Video-Response Questions

1 of 3

Application Screen: Video Response

You kick off a customer engagement where the customer's account of how frontline work gets done conflicts with what you observed on site. In your response, walk through what you would say to the customer, product lead, and engineers to separate observed evidence from assumptions and agree the next question to investigate.

Candidate experience

REC
0:42 / 2:00
1Record
2Review
3Submit

Response time

2 min

Format

Recorded video

Stage 2 · Resume Screening

Read the resume against fixed criteria

Reviewers score every application that clears the door against the same criteria. Stronger reviews advance to live interviews; weaker ones are archived without further screening.

Resume Review Criteria

8 criteria
Resume shows direct observation of people doing real work — shadowing tasks, recording sequences, interruptions, workarounds, and environmental constraints — rather than relying on secondhand requirements.
Resume shows moderating realistic task sessions with representative users, recording what happened without coaching, and using findings to change the design.
Resume shows structuring workflows into testable information architecture and prototypes at a fitting fidelity — sketches, interactive designs, or working builds — iterated with users and engineers.
Resume shows checking builds for keyboard use, screen-reader paths, contrast and reflow behavior, logging defects, and correcting them alongside engineers.

Does the resume show relevant prior work experience?

Is the resume complete, well-organized, and free from formatting, spelling, and grammar mistakes?

Does the resume indicate required academic credentials, relevant certifications, or necessary training?

Does the cover letter or personal statement convey clear relevance and familiarity with the job?

Stage 3 · During Interviews

Where the hire is decided

Interview rounds use the competency and attitude questions outlined above, then add tests, work simulations, and presentations that reveal deeper evidence about how the candidate thinks and works.

Presentation Prompt

Walk us through two pieces of tested frontline workflow work — for example an interaction flow and a usability finding you acted on — and talk us through how you observed the task, what you changed, and how you verified the result with users and engineers.

Format

portfolio-walkthrough · 20 min · ~2 hr prep

Audience

Hiring manager, peer designer, and an engineer partner in a working-session format with time for questions.

What to prepare

  • Select one or two existing workflow examples you are permitted to share, with before-and-after flow excerpts
  • Note in your own words the observed task evidence and usability result behind each example
  • Allow about two hours total to gather material and talking points; no new concepts required

Deliverables

  • A short verbal walkthrough of your examples with annotated portfolio excerpts
  • A brief discussion of what you tested, what changed, and what you would check next

Ground rules

  • Use only work you are permitted to share; redact customer-identifying detail
  • No new design work is expected — bring existing material only
  • Flag any recording or confidentiality limits before you begin

Scoring anchors

Exceeds
Links every claim to observed tasks and measured task outcomes, reconciles counter-evidence openly, and shows verified correction with clear residual risks.
Meets
Grounds the main choices in observed tasks and retest evidence with coherent pairing and follow-up reasoning.
Below
Narrates artifacts without task evidence; cannot explain test coverage, retest results, or engineering tradeoffs.

Response time

20 min

Positive indicators

  • Traces each design choice to a specific observed task, interruption, or workaround
  • Explains scenario design, participant coverage, and severity reasoning in plain terms
  • Shows how a correction was retested with representative users and what remained open
  • Describes pairing with engineers on constraints with acceptance examples

Negative indicators

  • Describes outcomes without citing any observed task, interruption, or workaround
  • Attributes success to visual polish while task failures go unaddressed
  • Cannot explain how a correction was retested or what residual issue remained
  • Treats engineer or research-partner input as an afterthought rather than joint evidence

Work Simulation Scenario

Scenario. You are the Forward Deployed Product Designer embedded in a customer delivery pod for a frontline-operations product. This session continues the scheduled Peer Technical — Prototyping and Delivery Pairing round (Build-constraint pairing exercise). You meet with Devon Park, a frontend engineer, who reports that the agreed focus order and offline save-resume behavior for the shift-handover workflow on shared tablets is costly to implement as specified before the next customer pilot. Field notes show frequent interruptions, shared devices, and partial connectivity. Drive the discussion toward a decision the team can implement and verify.

Problem to solve. Decide how to resolve the build constraint without losing task continuity for interrupted shift work, and walk us through your approach, including what you would verify in the next working build and what you would record as the updated decision with acceptance examples.

Format

stakeholder-roleplay · 40 min · ~2 hr prep

Success criteria

  • You compare at least two feasible alternatives against observed task sequences and interruptions.
  • You agree an updated interaction decision with concrete acceptance examples and named verification steps.
  • You preserve keyboard focus order, saved work, and clear next actions for pending and conflicting states.

What to review beforehand

  • Excerpt of task observation notes from the shift-handover workflow (provided, 2 pages).
  • Current interaction state map for save, resume, and offline behavior (provided).
  • Acceptance scenario register entries for the affected task paths (provided).

Ground rules

  • This is a live working discussion, not a take-home deliverable; no polished artifact is expected.
  • You drive the conversation: ask questions, state assumptions, and narrate tradeoffs out loud.
  • You may take brief notes, but decisions must be discussed and agreed in the room.

Roles in scenario

Devon Park, Frontend Engineer (peer, played by peer)

Motivation. Ship a pilot that frontline staff can complete without losing work, while staying honest about what the build can support on time.

Constraints

  • Must ship the pilot on the agreed date with the current engineering capacity.
  • Cannot accept an approach that silently drops saved work or breaks keyboard task completion.

Tensions to introduce

  • Presses on build effort: the full specified behavior needs two extra cycles the pilot does not have.
  • Reveals mid-session that a proposed shortcut would regress a previously fixed focus-order defect.

In-character guidance

  • Answer honestly when the candidate asks about effort, affected states, or regression risk; share the provided build facts.
  • Introduce the tension that the full focus-plus-offline behavior costs two extra build cycles, and press for what can be safely sequenced.
  • If the candidate states assumptions, confirm or correct them from the provided notes.

Do not

  • Do not solve the interaction problem for the candidate or propose the preferred compromise yourself.
  • Do not escalate hostility or shut down options; stay candid but collaborative.

Scoring anchors

Exceeds
Frames the tradeoff crisply, secures agreement on a task-grounded alternative with acceptance examples, and defines verification plus residual-risk tracking that engineering can act on immediately.
Meets
Reaches a workable decision with at least one compared alternative, acceptance examples, and a clear verification step in the next build.
Below
Agrees to a change without examining task impact, or restates the original interaction without resolving the constraint; verification and follow-up are missing.

Response time

40 min

Positive indicators

  • Asks high-information questions about the constraint, affected task paths, and pilot risks before proposing options.
  • Compares feasible alternatives explicitly against interruptions, shared devices, and connectivity limits.
  • Specifies acceptance examples and verification steps tied to the next working build.
  • Records the updated decision with rationale and flags residual risks for follow-up.

Negative indicators

  • Accepts the first proposed compromise without comparing alternatives against observed tasks.
  • Dismisses the constraint or insists on the original interaction without engaging feasibility.
  • Leaves verification vague with no acceptance examples or named owner for re-checking.
  • Overlooks focus order, saved work, or pending-state feedback in the agreed outcome.

Progression Framework

This table shows how competencies evolve across experience levels. Each cell shows competency at that level.

Delivery Partnership and Responsible Practice

4 competencies

CompetencyJuniorMidSenior
AI-assisted design and responsible verification

Checks approved AI processing and retention rules before using AI assistance, generates alternatives compared against observed tasks, and manually verifies outputs before user testing.

Pairs with engineers on AI-assembled working prototypes using behavior examples, prototypes review, correction, and fallback controls for AI suggestions, and tests them with representative users.

Evaluates AI-assisted pilots on effort and observed quality across engagements, proposes bounded reuse with verification guardrails, and verifies data-handling compliance.

Implementation pairing and defect resolution

Pairs with engineers to resolve build constraints against feasible alternatives, inspects complete task paths in working builds, and records reproducible discrepancies.

Leads constraint tradeoffs during implementation pairing, reviews revised components against states and regression evidence, and verifies corrections with manual checks.

Isolates shared causes of recurring cross-engagement defects with engineers, verifies reusable fixes, and coaches teams on regression-review practice.

Reusable pattern and design-system stewardship

Compares existing patterns against the observed task before adding customer-specific components, and maps local styling to shared tokens with accessibility review.

Proposes minimal component extensions with behavior and accessibility review, and teaches proven patterns through task examples and paired adoption.

Leads cross-engagement pattern proposals with evidence, exception criteria, and adoption plans, and agrees staged consolidation of duplicated patterns.

Scope-boundary negotiation and engagement transition

Documents interface consequences of policy-dependent usability problems and routes policy choices to responsible owners rather than redesigning policy unilaterally.

Agrees bounded workflows, outcomes, and decision owners at kickoff and transition, transferring tested decisions, unresolved risks, and follow-up measures to named owners.

Negotiates engagement scope and transition terms across parties, verifies handover completeness with feedback channels, and confirms named ownership of risks and measures.

Field Discovery and Usability Evidence

4 competencies

CompetencyJuniorMidSenior
Cross-customer synthesis and pattern scoping

Compares a bounded workflow against shared evidence under guidance, documenting where local exceptions apply rather than asserting reuse.

Compares field evidence across customers within an engagement to propose shared needs with explicit exceptions for product and engineering review.

Leads cross-engagement comparison of recurring requests, scoping reusable directions with exception criteria agreed with affected designers and partners.

Field discovery and workflow analysis

Arranges frontline access with consent and recording limits, observes real task sequences and interruptions, and turns observations into evidence-linked opportunities with review on unfamiliar risks.

Independently frames ambiguous multi-role workflows, reconciles conflicting stakeholder accounts against field evidence, and agrees the next investigation question with product and engineering.

Synthesizes discovery evidence across engagements to validate shared workflow directions, and coaches designers on ethical observation and evidence-linked synthesis.

Live-use measurement and continuous improvement

Observes first-use tasks during rollout, coordinates targeted guidance with the adoption owner, and reconstructs reported issues with users or support to separate defect types.

Agrees task-success and adoption measures with privacy-aware events, triangulates analytics with field evidence, and recommends prioritized improvements in product review.

Compares local task outcomes as shared patterns roll out, agrees adjustments with affected teams, and verifies post-release corrections against baselines across engagements.

Usability evaluation and evidence-based iteration

Defines unbiased task scenarios, recruits representative participants, pilots sessions, and retests corrections, escalating high-risk release judgments.

Designs evaluation plans covering interruptions and device conditions, ranks breakdowns by task impact, and reviews residual failures with product and engineering before release.

Sets evaluation strategy across engagements, verifies that serious corrections generalize, and uses residual-failure evidence to prioritize shared improvements.

Interaction Architecture and Resilient Interfaces

4 competencies

CompetencyJuniorMidSenior
Form, state, and failure-resilient design

Minimizes form burden with plain-language labels and recoverable validation, and designs loading, empty, confirmation, and retry states that preserve user work.

Designs freshness, conflict-resolution, offline, and timeout behavior with engineers, and verifies save, resume, and handover cues under interruptions and shift changes.

Sets failure-resilience expectations across engagements, verifies that state and recovery patterns hold under concurrent and offline conditions, and spreads proven approaches through shared patterns.

Inclusive and accessible design evaluation

Plans access-needs coverage, manually traverses key tasks by keyboard, verifies names, roles, announcements, and zoom and reflow behavior, and records barriers automated checks miss.

Conducts task sessions with people with relevant access needs, triages corrections with engineers by user impact, and escalates live barriers blocking essential work immediately.

Agrees shared component corrections for defects recurring across customers, coaches teams on manual review using affected task examples, and tracks cross-engagement accessibility improvement.

Information architecture and retrieval design

Models work objects and organizes navigation, labels, and record views around user decisions, validating findability with realistic data under review.

Translates approved access rules into interface states, facilitates taxonomy alignment across conflicting labels, and validates retrieval concepts with realistic datasets.

Leads shared information architecture proposals across engagements, agreeing essential local paths and adoption with product, engineering, and affected designers.

Rapid prototyping and interaction design

Chooses sketch, interactive, or paired-working-prototype fidelity to match the question, and specifies transitions and focus behavior with engineers in a shared model.

Prototypes competing interaction paths to resolve uncertain directions in short user sessions, and defines responsive and motion behavior with engineering acceptance examples.

Judges prototype investment across engagements, verifies that chosen directions rest on observed tasks, and spreads effective fidelity practices through pairing and critique.