مهارات البرمجة

Multi-Task Learning test for hiring

See how candidates reason about models that learn several prediction tasks at once.

10 دقيقة
15 سؤالًا
Advanced
Multiple choice
Questions in الإنجليزية
Phone and desktop

Use this test free See the question formats

الإجابة المختصرة

The Multi-Task Learning test is an advanced hiring assessment for machine learning engineers, applied scientists, data scientists, AI researchers, and senior engineers. It measures how candidates design, train, adapt, and evaluate models that share learning across tasks, including how they balance losses, handle task data, transfer knowledge, and compare results with single-task baselines.

ما الذي يقيسه

Designing, training, adapting and evaluating machine learning models that share learning across several tasks.

Architecture and loss balancing

Candidates consider how model structures share learning across tasks and how to balance the losses for those tasks.

Task design and data handling

Questions assess how candidates divide a problem into tasks and handle the data for them.

Transfer and domain adaptation

Candidates consider how knowledge can carry across tasks and domains.

Evaluation of multi-task models

Questions cover judging multi-task model results against single-task baselines.

Question formats

Multiple choice

The candidate: Reads a short situation, table or statement and picks the right answer, or every right answer.

You see: Whether the candidate knows the material well enough to apply it, not just recognize it.

15 questions in 10 minutes, served one at a time.

Roles this test fits

Machine learning engineers, applied scientists, data scientists, AI researchers and senior engineers who build models serving more than one prediction task.

What this test can show

Reasoning beyond a resume
Short PyTorch-style snippets and small result tables let you review how a candidate reasons about shared model structures and loss balancing.
A view of task choices
Use responses about splitting a problem into tasks and handling data to discuss design decisions relevant to models serving several prediction tasks.
Evidence across domains
Scenarios draw on areas including retail demand, medical imaging, speech, robotics, and text, so no single industry is favored.

What you get for each candidate

Example report (fictional data)Multi-Task Learning (MTL)

68‎%‎Percentile 40. Benchmark: HireValid candidates, n = 1,218

Architecture and loss balancing55‎%‎

Task design and data handling64‎%‎

Transfer and domain adaptation65‎%‎

Evaluation of multi-task models86‎%‎

Integrity Scoreثقة عالية

Stayed on the test tab throughout
No pasted answers
Answered at a steady pace

Scores, profiles and integrity signals are evidence for you to review alongside an interview, never an automatic decision.

Use this test free

Test controls and review signals

Each candidate receives a different set of questions, presented one at a time, with question order and answer options shuffled. Timing runs on the server, and reloading restores the same question with only the time left. Question text cannot be copied or selected. HireValid records signals such as time away from the test tab, pastes into answers, and answers faster than a question can be read. An Integrity Score and evidence timeline summarize these signals for employer review; neither decides on its own. With candidate consent, an employer can add webcam photos at random moments.

كيف نمنع الغش باستخدام الذكاء الاصطناعي · Integrity signals

Use the score as evidence

Review the score alongside a work sample and a structured interview, not as a hiring decision by itself. Ask candidates to explain choices in the work sample and compare those explanations with their responses about task data, transfer, and model evaluation. You can also consider related assessments such as Machine Learning, Deep Learning, and Computer Vision when relevant to the role.

أسئلة المقابلة المنظّمة

Think of a project where several prediction tasks could share one model. How did you decide which tasks should share structure?
Listen for a concrete design rationale, task relationships, and how the candidate would assess the choice.
Tell me about a time task data shaped your approach to a model serving several predictions.
Listen for how the candidate divided the problem into tasks and handled the data.
How would you check whether knowledge learned for one task or domain helps another?
Listen for a proposed comparison and evidence that could inform the decision.
How would you compare a multi-task model with single-task baselines before choosing one?
Listen for relevant evaluation choices and how results would guide the next step.

Understand scoring and benchmarks

Fairness and accommodations

Scenarios come from many domains, such as retail demand, medical imaging, speech, robotics and text, so no single industry is favored, and every fact a question needs is stated in it. Code is short, uses common Python and PyTorch idioms explained where they are unusual, and the arithmetic is light enough for scratch paper. The wording is plain US English with no idioms, so the test measures machine learning judgment rather than reading speed.

You can give any candidate extra time as an accommodation. Results are evidence for a person to review, never an automatic decision.

Provisional: reliability is reported once 200 candidates have completed it.

اقرأ نهجنا في الإنصاف

Test details

An advanced, timed check of how well a candidate designs, trains and judges models that learn several tasks at once. Questions cover sharing structures and loss balancing, splitting a problem into tasks and handling their data, carrying knowledge across tasks and domains, and comparing multi-task results with single-task baselines. Some questions show short Python snippets written in PyTorch style, alongside small tables of results.

Questions are in English.
Aids allowed: Scratch paper.
Each question is answered once, in order, with no going back.
4 practice questions, 4 minutes, before the timed test.

PyTorch is a trademark of its owner. HireValid is not affiliated with or endorsed by it.

الأسئلة الشائعة

What makes the Multi-Task Learning test fair across industries?+

Its scenarios draw on retail demand, medical imaging, speech, robotics, and text, so no single industry is favored. Each question states the facts needed, and plain US English keeps the focus on machine learning judgment.

Can candidates use scratch paper on the Multi-Task Learning test?+

Yes. Scratch paper is allowed, and the arithmetic is light enough for it. The test uses multiple-choice questions, including short PyTorch-style snippets and small tables of results.

Which roles fit the advanced Multi-Task Learning test?+

It is intended for machine learning engineers, applied scientists, data scientists, AI researchers, and senior engineers who build models serving more than one prediction task. At the advanced level, it suits candidates who already design and train models, not people new to machine learning.

How do candidates experience the Multi-Task Learning test?+

The test has 15 questions in 10 minutes and cannot be navigated backward. Candidates can try a practice set of four questions in four minutes before starting.