What Talented’s recent software-engineering searches required
We reviewed the four software-engineering role profiles in Talented’s hiring system as of September 2026. All four required candidates to use AI coding tools without handing technical judgment to them. Three named API or integration work, three included a substantial database layer, and three explicitly called out testing, debugging, security, or production reliability.
Across those four searches, the system recorded 1,774 application records, 364 submitted applications, 341 reviews, 37 next-step invitations, 26 completed next steps, and 15 booked interviews. These are funnel totals from a limited first-party sample, not universal hiring benchmarks.
The production AI developer scorecard
| Dimension | Evidence to inspect | Warning sign |
|---|---|---|
| End-to-end delivery | Can move from an ambiguous business problem through design, implementation, release, and verification | Describes a demo or isolated code contribution but cannot explain how it reached users |
| APIs and data flow | Can trace requests, webhooks, jobs, database writes, third-party failures, and retries across the system | Treats every integration as a successful request followed by a database insert |
| AI-system judgment | Chooses where a model belongs, defines acceptable output, handles uncertainty, and knows when deterministic code is safer | Adds an LLM to every step without defining failure behavior or evaluation criteria |
| Testing and evals | Uses automated tests, representative examples, regression cases, and human review where correctness is subjective | Judges quality from a few hand-picked prompts or a polished demo |
| Reliability and operations | Designs for idempotency, concurrency, rate limits, timeouts, recoverable failures, observability, and rollback | Can explain the happy path but not duplicate events, partial failure, or recovery |
| Security and privacy | Protects credentials and personal data, scopes access, verifies external requests, and preserves auditability | Logs secrets or user data, trusts client input, or relies on a model to enforce authorization |
| Communication | Explains assumptions, tradeoffs, remaining risk, and the evidence that would change the approach | Hides uncertainty behind framework language or cannot defend a generated solution |
How to test RAG systems
A RAG assessment should not stop at whether the app returns a plausible answer. Give the candidate a small knowledge set with conflicting, stale, restricted, and unanswerable material, then inspect how they define success.
- Chunking and retrieval choices tied to the actual document and question patterns
- A representative evaluation set that includes answerable, ambiguous, and unanswerable questions
- Grounding or citation checks that distinguish retrieved evidence from model invention
- Access controls that prevent retrieval across customers, teams, or permission boundaries
- Latency, model cost, retrieval quality, and fallback behavior measured separately
- A regression process for changes to prompts, embeddings, indexes, rerankers, or models
The strongest answer defines what a correct refusal looks like. A system that confidently answers outside the available evidence has failed even when the prose sounds good.
How to test agents and tool-using workflows
Agentic work adds state, permissions, and side effects. The candidate should be able to show where autonomy stops and how a failed or repeated tool call is contained.
| What to probe | What strong evidence sounds like |
|---|---|
| Tool permissions | The agent receives the minimum actions and data required for the current task |
| Approval gates | Irreversible, expensive, financial, or externally visible actions wait for explicit approval |
| State and recovery | Work can resume safely after a timeout, provider error, worker restart, or duplicated event |
| Evaluation | Success is measured against task completion, policy compliance, tool correctness, and harmful side effects rather than prose quality alone |
| Observability | Each decision and tool result is traceable without putting secrets or sensitive user data into logs |
A real assessment pattern: repair an inherited integration
One Talented technical assessment uses a deliberately broken production-style service rather than a blank coding prompt. The service receives signed lead webhooks, stores events, and creates contacts in a mock CRM. Duplicate delivery, concurrent requests, and temporary CRM failures are part of the problem.
Candidates have to verify the raw request correctly, prevent duplicate side effects, keep failed events recoverable, finish an operator retry flow, add tests, and produce useful logs without exposing credentials or personal data. They also explain root causes, remaining risks, and which AI-tool suggestions they accepted, changed, or rejected.
That one exercise reveals API reasoning, database state, security, concurrency, recovery, testing, operator experience, and communication. It is much closer to production engineering than asking someone to build another chatbot from scratch.
AI developer interview questions that reveal production judgment
- Walk me through a production AI feature from the original problem to release. What failed after real users touched it?
- A webhook can arrive twice at the same time and the downstream API is intermittently failing. How do you prevent duplicate side effects without losing the event?
- A RAG answer sounds convincing but cites the wrong source. How do you determine whether retrieval, ranking, context construction, or generation caused it?
- An agent has permission to send a message or change customer data. Where do you add approval, idempotency, and an audit trail?
- Show me a suggestion from Claude, Codex, Cursor, or another coding tool that you rejected or rewrote. What evidence told you it was unsafe or incorrect?
- You inherit an unfamiliar application with sparse documentation. What do you inspect before proposing a rewrite?
Follow each answer into a real system, constraint, decision, test, and observed result. Framework vocabulary is not evidence until the candidate can connect it to behavior.
Claude, Codex, and Cursor are allowed — judgment is the test
Blocking AI tools creates an artificial exercise for roles that use them every day. Let candidates work with the tools they would normally use, then inspect whether they understand and control the result.
- Require the candidate to explain the architecture and every material security or reliability decision
- Ask what the tool suggested, what they changed, and what they rejected
- Introduce a failing test or changed requirement and watch how they investigate
- Review the diff, tests, commit history, and handoff rather than scoring output volume
- Use a short live follow-up to confirm the candidate can modify the work without a rehearsed script
Why the résumé and technical assessment stay separate
Talented’s first-party analysis of 729 paired résumé and work-sample evaluations found a correlation of 0.03 between the two scores.
The résumé still helps verify relevant environments and career evidence. The assessment answers a different question: can this person reason through work that resembles the system they would be hired to improve? Do not turn either score into a substitute for the other.
How to run the assessment fairly
- Give every finalist the same starter system, scenario, time box, submission requirements, and scorecard
- Use synthetic records and a sandbox rather than production credentials or customer data
- Pay finalists and keep the task narrow enough to finish within the stated time
- Score observable behavior and tradeoffs instead of rewarding the largest rewrite
- Require tests, a concise handoff, and a short walkthrough of the most important decisions
After the scorecard
The client interview begins after relevant technical evidence exists. Talented targets a short shortlist within 14 days, charges when you hire, and includes a 90-day replacement.
FAQ
Should AI developers be allowed to use Claude, Codex, or Cursor during the assessment?
Yes, when those tools reflect the job. Ask candidates to explain, test, and modify the result and to identify at least one suggestion they changed or rejected.
Can you test our stack?
Yes. The assessment should follow the production responsibility and can use a small starter system that mirrors the relevant APIs, data flow, framework, or failure modes without exposing your live environment.
How do you assess RAG or agent experience?
Use a role-specific scenario. For RAG, inspect retrieval quality, grounding, access boundaries, evals, and refusal behavior. For agents, inspect tool permissions, approval gates, state, recovery, side effects, and task-level evaluation.
Does the best technical assessment require the most code?
No. A smaller fix with accurate diagnosis, tests, safe recovery, and a clear handoff can be stronger than a broad rewrite that introduces new risk.
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