Why pre-screen AI engineers before the technical panel
Training a model on a prepared dataset is a solved exercise. The job starts when accuracy sits below what the product needs and the cause could be the labels, the features, the split, the objective or the problem being wrong. Engineers worth hiring have a systematic way through that. A short screen asks how they debug a model that will not improve, which sorts candidates quickly.
What actually matters when screening AI Engineer candidates
- 01
Technical proficiency
Probe hands-on depth with transformer fine-tuning (LoRA, QLoRA), embedding stores like pgvector or Pinecone, PyTorch training loops, and orchestration via LangGraph or bare API calls.
- 02
Systems and trade-offs
Test how they weighed RAG versus fine-tuning versus prompt engineering, chose context window sizes, handled latency budgets, GPU spend, and fallback behaviour when a model provider degrades.
- 03
Evidence and rigour
Assess how they measured model quality: eval sets, LLM-as-judge pipelines, hallucination and retrieval recall metrics, regression suites, offline versus production A/B results.
- 04
Collaboration and communication
Look for work with product managers, domain experts labelling data, and platform teams; ask how they explained model limitations and non-determinism to non-technical stakeholders.
Pre-screening questions to ask AI Engineer candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Models in production
3 questions01Describe a machine learning project you worked on from start to finish.
Listen forProblem framing through to deployment, with the business measure it moved described honestly.
Projects that ended at a notebook, or accuracy reported with no downstream outcome.
02Describe your experience with natural language processing applications.
Listen forReal applications with evaluation on their own data, and known failure modes described specifically.
Experience limited to calling an interface, or no evaluation beyond published benchmarks.
03What is your experience with computer vision applications?
Listen forDeployed vision work with data collection conditions understood, including lighting and camera variation.
Models trained on clean datasets only, or performance drop in the field never investigated.
Fundamentals hold up
4 questions04Can you explain the difference between supervised and unsupervised learning?
Listen forA clear explanation with an example of choosing between them for a real problem they faced.
Definitions recited without application, or unsupervised methods proposed where labels exist.
05Can you explain gradient descent and its role in training?
Listen forUnderstanding beyond the definition, including learning rate effects and why training can stall.
Explanation limited to a textbook definition, or optimisation problems never encountered.
06How do you handle data preprocessing for training a model?
Listen forLeakage guarded against, with transformations fitted on training data only and applied consistently.
Scaling or encoding fitted before splitting, or leakage never considered as a risk.
07What methods do you use for hyperparameter tuning?
Listen forSearch run against a validation set with the cost considered, and results not tuned on test data.
Tuning against the test set, or search run without any budget on time or compute.
Debugs methodically
3 questions08How do you approach debugging a model that is not performing as expected?
Listen forErrors inspected case by case, with data, labels and objective all examined before changing the model.
A larger model reached for first, or failing examples never looked at individually.
09How do you validate the accuracy and robustness of your models?
Listen forPerformance broken down by segment, with behaviour under shifted or adversarial input examined.
A single aggregate metric reported, or robustness never tested outside the training distribution.
10Can you give an example of optimising a model for performance?
Listen forLatency or size reduced with the accuracy cost measured, and the trade-off stated to stakeholders.
Optimisation claimed without measuring accuracy loss, or improvements never verified in production.
Honest evaluation
2 questions11Explain the process of deploying a model to a production environment.
Listen forServing, monitoring and rollback all covered, with training and serving consistency handled.
Deployment described as handing over a file, or no monitoring after release.
12How do you ensure responsible use of AI in your projects?
Listen forPerformance checked across affected groups, with a case where they raised a concern about a use.
Ethics described as a principle, or no example of a use they questioned or declined.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Technical proficiency
35%5Names specific models, tokenizer quirks, quantisation choices and inference costs, and explains why they picked each over alternatives.
Systems and trade-offs
25%5Describes concrete trade-offs with numbers: p95 latency, cost per thousand calls, accuracy delta, and the reasoning behind the final architecture.
Evidence and rigour
25%5Built a real eval harness before shipping, cites baseline versus improved scores, and admits where metrics failed to capture user harm.
Collaboration and communication
15%5Gives examples of resetting expectations on accuracy, running labelling sessions with domain experts, and documenting known failure modes clearly.
The job starts when accuracy sits below what the product needs. A one-way video screen asks how they work through that.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for this role take?
Fifteen minutes across eight to ten questions, answered async. Enough to establish models they shipped, test the fundamentals, and hear how they debug and evaluate a system.
Should this replace a technical exercise?
No, it decides who gets one. The screen filters out candidates whose experience stops at tutorials, so the exercise goes to people who have taken something into production.
Evaluating answers
What is the strongest signal when screening this role?
How they debug a model that will not improve. Engineers with production experience work through data, labels and objective systematically. Anyone who reaches for a bigger model is guessing.
How do I judge their evaluation practice?
Ask what their model gets wrong. Real answers describe failure modes by segment. Anyone who reports a single accuracy number has not looked at where the system breaks.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
