AI Engineer Take-Home Test Examples and Evaluation Rubric

Most take-home tests for AI engineers measure willingness to spend a weekend on the problem, not the engineering judgment to solve it well — which is a poor proxy for what production AI work actually requires. The engineer who builds a clever RAG system in four hours and documents the tradeoffs honestly is more valuable than the one who spends twelve hours on a polished but overengineered solution that cannot be maintained. The test design is the problem, not the candidates.

This guide gives you five complete take-home test frameworks — each with a full problem statement ready to send to candidates and a scoring rubric ready for reviewers. The frameworks cover the five scenarios most commonly encountered in production AI engineering: retrieval-augmented generation, evaluation harness design, agent state machine construction, deployment and monitoring, and computer vision inference. Each is scoped to a realistic time window and scores engineering judgment over output volume.

Why Do Most AI Engineer Take-Home Tests Fail to Screen for What Matters?

The structural problem with most AI engineer take-homes is the scoring model, not the prompt. Reviewers default to rewarding complexity — more features, more elaborate architectures, more code — because complexity is easy to see and simplicity requires more judgment to evaluate. The result is a selection filter that favors engineers who can spend uninterrupted weekend time rather than engineers who make good decisions under real working constraints.

A second failure mode is the absence of an evaluation component. Production AI engineering is not about building systems — it is about knowing whether the systems work. Stanford's AI Index 2026 notes that evaluation methodology is cited as the primary skill gap in AI engineering hires by 61% of engineering managers surveyed. A take-home that does not require the candidate to evaluate their own output misses the most important signal available.

The third failure mode is prompt vagueness. Phrases like "build a chatbot" or "implement a RAG system" without specifying the corpus size, the evaluation criteria, the expected latency, or the deployment target give candidates nothing to push back against — and pushing back on a poorly specified problem is itself a production engineering skill worth measuring. The five frameworks below specify each of these dimensions explicitly.

LinkedIn data shows AI engineer postings grew 143% year-over-year, and demand for agentic AI skills grew 280% according to Stanford's AI Index 2026, with approximately 90,000 active U.S. listings. The volume of candidates is rising faster than hiring teams' ability to evaluate them rigorously. A repeatable, rubric-backed take-home process is the only way to maintain signal quality at scale.

What Are the Five Take-Home Test Frameworks?

The artifact below is complete and copy-paste ready. Each framework includes the full problem statement as you would send it to a candidate, the time allocation, and the scoring rubric broken into weighted dimensions.

Framework 1 — RAG Implementation (4–6 Hours)

Problem Statement (send to candidate verbatim):

You will build a retrieval-augmented generation system over a provided document corpus. The corpus is 75 documents — a mix of PDF research papers and plain-text summaries — which you can download from [link]. You will also receive 15 evaluation questions with ground-truth answers.

Your deliverable is a Python package (FastAPI or CLI entry point, your choice) that:

1. Ingests and chunks the corpus. Document your chunking strategy and explain why you chose it.
2. Embeds chunks using any embedding model of your choice. Justify the choice in your README.
3. Answers each of the 15 evaluation questions.
4. Evaluates your own output: compute precision@3 and ROUGE-L against the provided ground-truth answers. Show the scores in your README.
5. Identifies the two questions your system answered worst and explains why.

Time allocation: 4–6 hours. Log your actual time and include it in the README.

What we are NOT looking for: a production-grade system, a fancy UI, or a system that achieves perfect scores. We are looking for engineering judgment in the decisions you made and honest analysis of where your system fails.

Submission: GitHub repo with a reproducible README. Must run with python main.py or uvicorn app.main:app.

Scoring Rubric:

Framework 2 — Evaluation Harness Design (3–4 Hours)

Problem Statement (send to candidate verbatim):

We have a broken RAG system. You will not fix the system — you will design the evaluation that would tell us exactly how broken it is and where.

We are providing: (a) the RAG system's source code [link], (b) 50 sample queries with expected answers, and (c) the system's current outputs for those 50 queries.

Your deliverable is a Python evaluation harness that:

1. Ingests queries, expected answers, and system outputs.
2. Computes at least three distinct metrics. Choose metrics appropriate to the failure modes you observe — justify your choices.
3. Produces a structured failure report: which queries failed, what type of failure (retrieval, generation, hallucination, formatting), and how you classified them.
4. Proposes three specific improvements ranked by expected impact, with reasoning.

Time allocation: 3–4 hours.

Scoring Rubric:

Framework 3 — Agent State Machine (4–6 Hours)

Problem Statement (send to candidate verbatim):

Build a two-tool agent that can answer questions about a provided dataset. The two tools are: (1) a SQL query executor against a SQLite database we provide [link], and (2) a web search stub that returns canned results from a provided JSON file [link].

Your agent must:

1. Accept a natural language question as input.
2. Decide which tool to call, call it, and incorporate the result.
3. Handle at least two error cases: SQL syntax errors (retry with corrected query) and tool unavailability (fall back gracefully with a clear message to the user).
4. Log every tool call, its input, its output, and the decision that followed. The log is your primary evaluation artifact.
5. Answer five provided test questions and include the logs for all five in your submission.

Time allocation: 4–6 hours. You may use any LLM API. Include your total API cost in the README.

Scoring Rubric:

Framework 4 — Deployment Pipeline (3–5 Hours)

Problem Statement (send to candidate verbatim):

We are providing a trained sentiment classification model — a fine-tuned BERT variant as a .pt checkpoint [link]. Containerize it and deploy it with a monitoring endpoint.

Your deliverable:

1. A Dockerfile that builds the model service. It must expose a /predict endpoint accepting {"text": "..."} and returning {"label": "...", "confidence": 0.0}.
2. A /health endpoint that returns model load status and last-inference latency.
3. A /metrics endpoint returning request count, p50 and p99 latency, and error rate since startup — in Prometheus text format.
4. A docker-compose.yml that runs the service and, optionally, a Prometheus/Grafana stack against it.
5. A one-page runbook: how to deploy, how to roll back, what the /metrics output means, and what alert thresholds you would set for production.

Time allocation: 3–5 hours.

Scoring Rubric:

Framework 5 — Computer Vision Inference (4–6 Hours)

Problem Statement (send to candidate verbatim):

We are providing a small labeled dataset of 500 images across five object classes [link]. Fine-tune a YOLOv8n model on this dataset and deploy it as a FastAPI endpoint.

Your deliverable:

1. A training script that fine-tunes YOLOv8n on the provided dataset. Log mAP@50 at the end of training.
2. A FastAPI endpoint that accepts an image (multipart upload or base64) and returns detected objects: {"detections": [{"class": "...", "confidence": 0.0, "bbox": [x1, y1, x2, y2]}]}.
3. A simple HTML test page (one file, no framework) that uploads an image and displays the JSON response.
4. A one-paragraph analysis: what your model got right, what it got wrong, and what you would do next to improve mAP.

Time allocation: 4–6 hours. GPU access is not required — YOLOv8n trains on CPU in under 30 minutes on this dataset size.

Scoring Rubric:

How Do You Use These Frameworks Effectively?

Send the framework as-is — do not add requirements mid-process or change the time allocation after the candidate has started. The value of a rubric is consistency: every candidate sees the same problem scored the same way.

Schedule a 30-minute code walkthrough within 48 hours of submission. The walkthrough is the second gate. Ask the candidate to walk you through one decision they made and one thing they would change if they had another two hours. Candidates who cannot explain their own code — regardless of submission quality — fail this gate. Per research on AI-assisted coding published in arXiv (2312.10997), the gap between AI-assisted output quality and the author's ability to explain that output is the most reliable signal for distinguishing genuine competence from tool-assisted mimicry.

Calibrate your scoring team before the first batch. Have two reviewers independently score the same submission, then compare. Disagreements above one point on any dimension need a tiebreaker rubric discussion. Calibration sessions take 30 minutes and save hours of downstream argument.

Do not penalize candidates for using AI coding tools. Every production AI engineer uses them. The walkthrough reveals whether the candidate understands the output — that is the competence you are measuring.

Comparison Table: Take-Home Test Frameworks at a Glance

How Does F5 Apply This Framework When Vetting AI Engineers?

Every AI engineer who enters the F5 pipeline completes a structured take-home drawn from the frameworks above before any client interview. The submission is scored by F5's technical review team against the rubric. Clients receive the scored submission alongside the candidate's profile — so the first interview conversation can start with the engineering work rather than background verification.

F5 maintains a database of 85,500+ candidates in our internal sourcing and screening database, with dedicated sourcing in Pune and Rajkot in India and Manila in the Philippines. AI and ML engineers are available in the range of $500–$950/week all-inclusive — within the canonical $375–$1,200 per week, all-inclusive range that covers salary, HR, equipment, and management. The $600/week anchor reflects the entry point for a mid-level AI engineer with 2–4 years of experience; the $31,200 annual minimum (at $600 × 52 weeks) compares against a U.S. AI engineer base salary of $160,000–$280,000, plus benefits, recruiting fees, and onboarding costs.

The take-home process reduces the client's interview burden: by the time a client interviews a shortlisted candidate, F5 has already verified that the engineer can complete a structured technical problem independently, explain their decisions under questioning, and produce a runnable artifact. Shortlists are delivered in 7–14 business days from engagement start. Replacements, if needed, are delivered in 7–14 days at zero cost, anytime.

To explore hiring remote AI and ML engineers through this process, or to understand how the model works for SaaS and technology companies, the starting point is a call with the F5 team. You can also review what to look for in an AI engineer before your first screening conversation.

For teams building internal AI engineering interview processes without using F5, these frameworks are free to use. The rubrics are calibrated against submissions from engineers who went on to succeed in production AI roles — the weighting reflects what actually predicted on-the-job performance, not what looked impressive in a portfolio review.

Frequently Asked Questions

Ready to skip the screening process entirely? F5's technical team runs these frameworks against every AI engineer candidate before the first client call. Hire remote AI and ML engineers with pre-scored take-home submissions included, or schedule a call with Joel Deutsch to see the process in detail. F5 serves 250+ companies with a 95% client retention rate, measured as clients who continue beyond the first 3 months, and delivers shortlists in 7–14 business days — all at $375–$1,200 per week, all-inclusive.