Most deep learning intern listings fail the same way: they describe a person rather than a job. Candidates cannot tell what they would do on Monday, so the strong ones apply somewhere clearer.
Deep Learning Intern is a well-defined brief, which helps at screening time: the skills below are specific enough that twenty minutes of questions will separate someone who has done the work from someone who has read about it.
People searching for deep learning intern often also look at neural network intern. The skills overlap heavily; what differs is emphasis, while neural network intern leans on neural and network. If your requirement genuinely spans both, say so in the listing rather than picking one title and hoping.
Below: the skills worth testing, the work a student can genuinely finish in a term, 2026 stipend bands, and questions that have a wrong answer. Posting is free and takes about two minutes.
What a single Deep Learning intern actually does in the first 90 days
Each of these is work a team member would otherwise do. That is the test of a good intern brief: real work already on someone's list, not a project invented to keep the intern busy.
- Fine-tune a pretrained model on our data and report the honest lift
- Set up experiment tracking so results stop living in screenshots
- Profile and fix the training loop that runs out of GPU memory
Deep Learning skills worth screening for
Screen on the first three. The rest are teachable inside a term, and treating them as entry requirements shrinks your pool for no gain.
- 1Network architecture fundamentals
- 2Backpropagation and optimisers
- 3Regularisation: dropout, augmentation, early stopping
- 4Transfer learning and fine-tuning
- 5GPU memory management and batch sizing
- 6Loss-curve reading
- 7Experiment tracking
The tools column is where CV inflation happens. Pick two and ask what went wrong the last time they used them; the answer is unfakeable.
Screening questions for deep learning intern
These separate practice from theory. Ask two, listen for a specific example, then follow the example rather than moving to the next question.
Your loss goes down and validation goes up. What now?
What a good answer shows: Overfitting diagnosis
Why fine-tune rather than train from scratch?
What a good answer shows: Practical resource awareness
Score every candidate on the same questions. Comparing free-form conversations across a shortlist is where inconsistency, and bias, get in.
Where the Deep Learning candidates come from
You are hiring from a verified pool of students and recent graduates across India: premium institutes and strong regional colleges both, with projects, skill tags and availability already on the profile. Every employer is verified before a listing goes live, which is why candidates here actually reply.
- Skill tags — filter directly on PyTorch or TensorFlow, CUDA, Weights & Biases and the rest of the Deep Learning stack
- City and willingness to relocate, or remote-only if the role is remote
- Institute tier, if a specific campus cohort matters for this role
- Portfolio and project evidence attached to the profile, rather than a résumé alone
- Graduation year and current semester, so you only see candidates free when you need them
Skill tags come from the candidate’s own projects and verified profile, so filtering on PyTorch or TensorFlow or CUDA returns people who have used them rather than people who listed them.
What to pay a single Deep Learning intern in 2026
₹20,000–₹48,000 a month is the band we see for this role across India. The spread is mostly city and company stage, not candidate quality.
Listings that state a stipend get noticeably more qualified applications than "as per industry standards", which candidates read as low or undecided.
Funded product startups often pay above large services firms for the same role, because they are competing for the same few candidates and can decide faster.
Add the reviewer’s hours, tooling access and a laptop if the role needs one. That is the true cost — and it is still far below a lateral hire.
Late stipends are the most common complaint from interns in India and they travel fast through campus groups. It costs you next year’s pool as well as this one.
Getting one Deep Learning intern to actually produce something
The difference between an intern who ships and one who does not is almost never talent. It is whether the work was ready on their first day and whether someone read it on their second week.
Laptop, accounts, repository or dataset access, and a task small enough to finish in two days. Interns who spend week one waiting for access rarely recover the momentum.
Read their work in the first week, not the fourth. Early correction on a small piece of Deep Learning work is cheap; late correction on a term’s work is not.
Someone who wants the output and will complain if it is wrong. Work with no audience is the fastest route to a disengaged intern.
Write down what a successful term would produce. Otherwise the end-of-term assessment becomes a memory of impressions, and that helps nobody.
How to post deep learning intern on MyInternships.in
The whole flow is a short chat. Company details are verified before the listing goes live, which is exactly why candidates trust and answer these listings.
Start with the outcome rather than the title: what you want finished by the end of the term. The assistant turns that into a Deep Learning listing.
Rather than a blank form, you get a draft to react to — which is faster, and produces a far more specific Deep Learning listing than most teams write from scratch.
Every employer is checked before a listing goes live. That verified badge is why candidates on this platform actually reply.
Applications land in your dashboard with skills and projects attached, so the first pass takes minutes rather than an afternoon of résumé reading.
Free plan: one listing, live after verification. Starter ₹499: five listings a month, published instantly, full applicant contact and résumé access. Growth ₹999: fifteen listings with AI candidate matching.
Mistakes that cost you the good Deep Learning candidates
None of these are hypothetical. They are the patterns behind listings that get plenty of applications and no hires.
A Deep Learning listing with fourteen required tools reads as a company that does not know what it needs. Strong candidates self-select out; the ones who apply anyway have inflated their CVs to match.
Requirement lists assembled from other postings read as generic and attract generic applications. Write what this person will actually do this term.
An intern who spends week one waiting for a laptop and accounts rarely recovers the momentum. Prepare day one before you make the offer.
Work that nobody reads produces an intern who stops trying by week four. Name the reviewer before you post, not after the offer is accepted.
Deep Learning Intern — frequently asked questions
What skills should deep learning intern have?+
The three that matter most are Network architecture fundamentals; Backpropagation and optimisers; Regularisation: dropout, augmentation, early stopping. Beyond those, look for working familiarity with PyTorch or TensorFlow, CUDA, Weights & Biases. Everything else on the list above is teachable inside a term — treating it as an entry requirement shrinks your pool without improving the hire.
What should we set as the goal for the term?+
One finished thing. Fine-tune a pretrained model on our data and report the honest lift is the right size: real work someone on the team would otherwise do, small enough to finish, visible enough to assess. If they move quickly, set up experiment tracking so results stop living in screenshots is the natural second piece. A term with three half-finished projects assesses nothing and teaches less.
How do we benchmark the stipend for deep learning intern?+
Start from ₹20,000–₹48,000 a month, then adjust for city and duration: metros at the top, tier-2 typically 25–40% lower, and six-month commitments above six-week ones. Publish the number in the listing — "as per industry standards" is read as low or undecided, and it costs you applications from exactly the candidates who had another option.
What is the fastest way to tell a strong Deep Learning candidate from a weak one?+
Ask about something that went wrong. "Your loss goes down and validation goes up. What now?" gets you overfitting diagnosis, and two follow-up questions on their own example will tell you the depth. Candidates who have only studied the topic run out of specifics almost immediately.
What does it cost us in time to supervise one Deep Learning intern?+
Realistically two to four hours a week of a competent person: a longer session early on, then short daily availability and a weekly review. Below that, the intern stalls and produces nothing you can use. Above it, you are doing the work yourself. That time is the true cost of the hire, and it is what the stipend line in your budget does not show.
What documents does a Deep Learning intern usually need at the end?+
Most Indian colleges ask for a completion or experience certificate, and many also require a mentor evaluation on the institution's own form. Ask which format the candidate's college needs during onboarding rather than in the final week — it takes two minutes then and becomes a scramble later.
Do we need a job description ready before posting deep learning intern?+
No. The posting assistant asks a few short questions — the role, the work, the duration, the stipend — and drafts the description, the title and the skill tags for you. You review and edit everything before it publishes, and you can paste in your own description if you already have one.
Can we hire deep learning intern remotely, or in a specific city?+
Both. The pool covers every major hiring city and hundreds of tier-2 and tier-3 towns, and the role can be posted as remote, hybrid or on-site. For Deep Learning work specifically, remote widens the pool considerably — filter on skill and availability rather than pin code unless the work genuinely requires presence.
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