Technical Interview Kit: Design and Code Questions for ML/Video Engineering Roles

Hook: Hiring for ML/Video Roles Is Broken — Here’s a Kit That Fixes It

Recruiters and engineering managers: you need candidates who can ship reliable video generation, recommendation, and moderation systems today — not academic proofs. Yet many interview processes still lean on trivia, toy problems, or vague system-design conversations that fail to predict on-the-job success. This kit gives you a practical, up-to-date collection of design prompts, coding tasks, take-home assignments, and scoring rubrics tailored for ML and video engineering roles in 2026.

Quick summary — what you’ll get

Use this article as a plug-and-play interview flow: stage-by-stage prompts for video generation, recommender, and moderation engineers; clear scoring rubrics you can copy; sample solutions and test harness ideas; and hiring tips that reflect late-2025 to early-2026 trends like multimodal foundation models, real-time on-device inference, and synthetic-content detection.

Focus interviews on product-driven engineering: operational constraints, safety trade-offs, and reproducible evaluation — the things that predict production success in ML video systems.

2026 context: Why these prompts matter now

The industry in late 2025–early 2026 is defined by three dynamics that change how you should interview ML/video engineers:

• Explosive vertical video growth — startups scaling short-form, mobile-first video have shown how different latency, encoding, and UX constraints are compared to long-form streaming.
• Generative-video commercial traction — new entrants and rapid valuations for companies offering click-to-video generation mean teams must design for quality, cost, and misalignment mitigation.
• Regulation and synthetic-content detection — governments and platforms demand content provenance, watermarking, and robust moderation pipelines.

That means interviews must probe for system-level thinking (bandwidth, cost, privacy), ML evaluation (perceptual metrics, bias testing), and safety-first design (watermarking, provenance).

How to use this kit

Pick items by role and interview stage. A recommended 4-stage flow for a mid-senior role:

1. Recruiter screen (30 min): culture fit, scope alignment, basic systems experience.
2. Technical phone (60 min): one coding + one short systems question.
3. Take-home assignment (48–72 hours): real dataset, reproducible evaluation, small deployable demo.
4. Onsite loop (2–4 interviews): deep system design, pair-programming, product & safety discussion.

Design interview prompts

Use these prompts to evaluate product thinking, trade-off awareness, and engineering rigor. Each prompt includes what to probe, an outline of an ideal answer, and a short scoring rubric.

1) Video generation system — “Build a scalable short-form AI video generator”

Prompt: Design an end-to-end service that converts a 20–60 second script + style seed into a vertical video optimized for mobile. Include pipelines for generation, quality control, watermarking, and live feedback for creators.

Probe:

• How would you structure the generation stack (text-to-video model selection, frame synthesis vs. latent-based approaches)?
• How do you ensure low cost and latency for preview vs. final render?
• How do you enforce content safety (profanity, hallucinated brand logos, deepfakes)?
• How would you implement provenance (watermarking, metadata signatures)?

Expected answer outline:

• Two-tier generation: fast, low-cost preview (lower frame-rate latent rollouts) + high-quality offline renderer.
• Batching and model quantization for inference; edge-enabled preview rendering for creator devices.
• Automated moderation stage with hybrid rules + multimodal classifiers; human-in-loop for edge cases.
• Cryptographic metadata signatures and visible/invisible watermarks; versioned model cards and provenance headers.

Scoring rubric (out of 10):

• Architecture clarity and trade-offs: 4
• Operational concerns (cost/latency): 3
• Safety/provenance solutions: 2
• Novelty & product insight: 1

2) Recommender system — “Personalized vertical feed at 100M DAU”

Prompt: Design a ranking & retrieval pipeline for a mobile-first feed that personalizes for short sessions and rapid content discovery. Consider cold-start creators, freshness, and real-time signals like watch-to-completion.

Probe:

• How would you balance retrieval vs. ranking? Which models for candidate generation?
• How do you handle feedback latency and non-stationary content? What offline metrics vs. online metrics matter?
• How would you detect and handle manipulation (bot-like engagement)?

Expected answer outline:

• Multi-stage pipeline: dense retrieval (embedding-based) + lightweight scoring + heavy neural reranker for top K.
• Online features from streaming ingestion (session-level), temporal decay on signals, and bandit-aware exploration (contextual MAB or RL).
• Robustness: anomaly detection on engagement spikes, smoothing, and fairness constraints to prevent creator cold-start exclusion.

Scoring rubric (out of 10):

• System design & scaling: 4
• Evaluation strategy (offline vs online): 3
• Fairness & manipulation mitigation: 2
• Clarity & reproducibility: 1

3) Moderation system — "Real-time multimodal moderation at scale"

Prompt: Design a moderation pipeline that filters harmful content in uploaded and generated videos. Include detection of policy violations, appeals workflow, and A/B-friendly model rollout.

Probe:

• What models and signal fusion techniques would you use for multimodal detection?
• How do you keep latency low while maintaining high precision for urgent policy cases?
• How will the pipeline support explainability and appeals?

Expected answer outline:

• Combination of fast heuristics (audio profanity filters, OCR on frames) + multimodal transformer classifiers for suspicious content.
• Priority queues for live removal candidates; human review interface with context-rich evidence and model confidence.
• Model interpretability via saliency maps; transcript linking to flagged segments; documented policies and a staged rollout with metrics for false positives/negatives.

Scoring rubric (out of 10):

• Multimodal detection & prioritization: 4
• Safety governance & appeals design: 3
• Operational reliability & latency plan: 2
• Explainability: 1

Coding interview prompts (30–60 minutes)

These questions test practical engineering skills you will need in production systems. Provide candidates with a laptop and real-time test harness or a notebook.

1) Efficient frame differencing (30 min)

Problem: Given a sequence of video frames represented as arrays, implement a function that outputs a compact set of segment descriptors for regions that change significantly. The solution should prioritize runtime and memory efficiency.

What to evaluate:

• Algorithmic complexity and trade-offs (sampling, block-level hashing).
• Memory-conscious implementation (streaming windows, quantization).
• Tests and edge cases (static scenes, flashing lights).

Scoring rubric:

• Correctness and robustness: 5
• Efficiency & clarity: 3
• Test coverage: 2

2) Mini ranking model (60 min)

Problem: Implement a small candidate scoring function that combines CTR predictions with freshness and diversity penalties. Provide a simple evaluation on a synthetic dataset and show how you would tune lambda parameters.

What to evaluate:

• Data handling and feature engineering.
• Clear separation of scoring logic and evaluation code.
• Use of realistic metrics (NDCG@k, session-level metrics).

Scoring rubric:

• Model correctness & reproducible evaluation: 5
• Code quality & parameter tuning approach: 3
• Interpretability: 2

Take-home assignments (48–72 hours)

Take-homes should be realistic but bounded. Provide a curated subset of public datasets, a Dockerfile, and a small test harness. Expect 8–16 hours of work.

Assignment A — Video generation mini-pipeline

Deliverables:

• Notebook or container that accepts a short text prompt and returns a 10–15s vertical video preview.
• Readme describing model choices, cost/latency estimates, and a small A/B plan for quality evaluation.
• Unit tests for reproducibility and a short demo GIF.

Evaluation criteria (weights):

• Reproducibility & docs: 25%
• Quality of generated preview & perceptual metrics: 35%
• Operational thinking (cost/latency): 20%
• Safety & watermarking approach: 20%

Assignment B — Recommender mini-project

Deliverables:

• Candidate retrieval + ranking pipeline on a synthetic or public short-video dataset.
• Evaluation comparing two ranking variants (baseline vs tuned) with full reproducible scripts.
• Short report describing handling of cold-start and an ethical risk assessment.

Evaluation criteria (weights):

• Reproducible evaluation & baseline clarity: 30%
• Metric choice & statistical significance: 30%
• Product thinking & cold-start solutions: 20%
• Ethics & bias testing: 20%

Assignment C — Moderation pipeline prototype

Deliverables:

• Small pipeline that ingests video + audio, runs quick heuristics, and outputs a ranked list of segments for review plus confidence scores.
• Calibration report showing precision/recall trade-offs and a plan for human-in-loop thresholds.
• Code for an evidence UI mockup (static HTML acceptable).

Evaluation criteria (weights):

• Detection performance & calibration: 40%
• Operational readiness & latency: 25%
• Explainability & UI for human reviewers: 25%
• Governance & appeal process outline: 10%

Timed onsite / pair-programming tasks

Run these as live collaboration to observe communication and real-time problem solving.

• Pair-design: 45 minutes. Co-design a flow for A/B testing a new reranker; interviewer plays PM and data engineer roles.
• Pair-code: 45 minutes. Fix a memory leak in a video ingest microservice and write a new unit test.
• Safety deep-dive: 30 minutes. Walk through a false-positive moderation incident and design root-cause analysis + monitoring.

Scoring rubrics — a reproducible framework

Use a weighted rubric to reduce bias and increase repeatability. Below is a canonical rubric for senior ML/video engineers. Scores on each dimension are 1–5; multiply by weight.

• Technical depth (weight 30%): model choices, scaling, performance.
• Engineering quality (20%): readable code, tests, CI, reproducibility.
• Product & systems thinking (20%): constraints, trade-offs, user impact.
• Safety & ethics (15%): bias testing, moderation, provenance.
• Communication & collaboration (15%): clarity, feedback handling, mentorship potential.

Example pass threshold: weighted score >= 3.6/5 (72%). Adjust for seniority.

Interviewer checklist & red flags

Use this quick checklist during debriefs.

• Does the candidate tie design choices to measurable metrics?
• Do they demonstrate trade-offs between latency, cost, and quality?
• Do they include safety mitigations and explainability in designs?
• Can they reproduce their take-home results and explain unexpected failures?

Red flags:

• Vague on deployment concerns (observability, rollback, CI/CD).
• No awareness of edge cases, adversarial attacks, or privacy constraints.
• Inability to explain evaluation choices or metrics clearly.

Evaluation metrics & 2026 best practices

In 2026, evaluation for ML/video systems extends beyond classical loss curves. Expect candidate conversations to reference:

• Perceptual similarity metrics (LPIPS-style variants tuned for video temporality).
• User engagement-quality trade-offs: NDCG, session-level retention, and creator-side metrics.
• Robustness tests: synthetic perturbations, content-manipulation adversaries, watermark resilience.
• Bias & fairness: demographic parity where relevant, creator-discovery fairness, and creator income impact analysis.
• Provenance & traceability: cryptographic metadata completeness and watermark detection rates.

Sample scoring matrix (real example)

For a senior ML/video candidate, collect scores from each interviewer and compute a weighted average. Example per-dimension mapping:

• Technical depth: 4/5 => 0.3 * 4 = 1.2
• Engineering quality: 3/5 => 0.2 * 3 = 0.6
• Product thinking: 4/5 => 0.2 * 4 = 0.8
• Safety & ethics: 5/5 => 0.15 * 5 = 0.75
• Communication: 4/5 => 0.15 * 4 = 0.6

Weighted total = 3.95/5 => strong hire recommendation.

Sample solution highlights (what to expect)

Good candidates will:

• Give concrete numbers for latency and cost (e.g., preview generation <= 2s, final render in 2–4 minutes), not just high-level phrases.
• Propose measurable monitoring (per-segment FPR, sample annotation cadence, watermark detection rate).
• Show familiarity with modern tooling (multimodal foundation models, ONNX/TFX, streaming platforms like Pulsar/Kafka or cloud equivalents) — but focused on trade-offs, not buzzwords.

Legal, safety, and hiring compliance notes (quick)

When testing generative systems or moderation corpora, ensure datasets and work products do not include private PII or copyrighted content unless you have clearance. Include nondisclosure and data-privacy clauses for take-homes when necessary.

2026 trends hiring teams should watch

• Shift toward foundation multimodal models tuned for video: expect candidate familiarity with fine-tuning and alignment techniques.
• Increased demand for on-device inference for previews and privacy-preserving features.
• Stronger regulatory attention on synthetic media provenance and platform responsibilities — hire for safety-minded engineering.
• Growing tooling for watermarking and cryptographic provenance — integrate these into take-homes and design prompts.

Final actionable takeaways

• Replace trivia with product-focused prompts that measure trade-off reasoning and operational rigor.
• Use bounded, real-world take-homes with reproducible evaluation and time limits (48–72 hrs).
• Score consistently with a weighted rubric that elevates safety and product thinking alongside technical depth.
• Make moderation, provenance, and latency first-class interview topics in 2026.

Call to action

Use this kit to build or refine your interview loop this quarter. If you want ready-to-run templates — downloadable scoring spreadsheets, Dockerized take-home scaffolds, and UI mockups for reviewer workflows — join the challenges.pro hiring toolkit or reach out to our community to get editable templates and a shared test-harness repository used by engineering teams in 2026.

Get started: pick one design prompt and one take-home this week, set a reproducible evaluation, and run a calibration session with two interviewers. You'll reduce hiring time and increase on-the-job prediction accuracy within a single hiring cycle.

Related Reading

• Negotiating with Cloud Providers for GPU Priority During Hardware Shortages
• Spotlight on Affordable Tech: Best Mid-Range Smart Lamps for UK Dining Rooms
• Accessibility by Design: How It Affects the Long-Term Value of Board Games
• How to add side-gig pet services to your CV without hurting your professional brand
• From AMA Replies to Rehab: Building a 12-Week Recovery Program Using Trainer Guidance