Build real projects.
Prove your skills.
Earn verifiable experience.
A remote engineering internship with weekly sprints, real deliverables, and evaluation against a published rubric. You finish with work you can show and a credential an employer can check.
Universities teach the theory. Industry hires on evidence.
Students graduate able to pass exams and stuck on their first real ticket. The programs that close that gap usually charge tuition, which excludes the people who need them most.
DeepTrics runs the internship the way a team would: a brief, a deadline, a review, and a record of what you actually produced. AI handles evaluation at a scale humans cannot, which is what lets us keep it free for every student, in any country.
From application to verifiable credential
Twelve stages, in the order they actually happen. No step here is aspirational.
- 01
Application
Pick a program and write a short statement of purpose.
- 02
Selection
Applications are reviewed and answered by email — approved, waitlisted, or not this time.
- 03
Offer
Approved interns receive a formal offer letter with program terms and dates.
- 04
Onboarding
Your workspace opens with week one already waiting — no delay between approval and work.
- 05
Weekly sprints
Weeks unlock in order. Finish one to open the next early, or wait for its date.
- 06
Assignment
Each week states its objective, its tasks, and exactly what you hand in.
- 07
Submission
Write up what you shipped and what you decided, and link the work itself.
- 08
AI evaluation
Your submission is evaluated against that week's plan — scored across five dimensions, with written feedback.
- 09
Revision
Act on the feedback and resubmit. The week is re-evaluated on the new work.
- 10
Human review
Request an expert reviewer when a week warrants one. Their review closes that week.
- 11
Completion
Your program is reviewed as a whole and marked complete.
- 12
Credential
A credential is issued with its own ID — verifiable by anyone, without an account.
Judged on demonstrated engineering, not completion.
Every submission is scored against the same published rubric, out of 100. You see the score and the reasoning each week, so you know what to change before the next sprint — not at the end.
Technical proficiency
Whether the work is correct, and whether the approach holds up under the constraints it was built for.
Delivery against the brief
How much of the week's stated objective actually shipped, judged against that week's plan rather than a generic checklist.
Communication
Whether someone who wasn't there could follow what you built, what you decided, and why.
Ownership
Initiative, follow-through, and how you handle the parts of the work nobody assigned you.
Learning
Evidence you went past the literal ask — and that last week's feedback shows up in this week's work.
AI does the evaluation. People do the judgement.
Reviewing every submission from every intern, every week, is not something a human team can do for free. That part is automated so the human attention goes where it counts.
What the AI evaluator does
- Evaluates every weekly submission against that week's specific plan, not a generic checklist
- Scores five defined dimensions and writes feedback naming what to change next
- Re-evaluates from scratch when you revise and resubmit
- Runs under a hard daily spend cap, so evaluation stays free to use
It evaluates submitted work. It is not a chat assistant, and it does not hold ongoing conversations or review code on request.
What people do
- Author the curriculum every intern works through
- Review any week an intern escalates, and close it with their own assessment
- Decide applications, completion, and whether a credential is issued
Expert review is requested per week, when the work warrants a second opinion — not automatic on every submission.
A credential an employer can check themselves.
Finish the program and a credential is issued with its own permanent ID, printed on your certificate alongside a QR code. Anyone can confirm it — no account, no request to us. It shows the program, its length and the completion date, and never your contact details or your individual scores.
- 1Complete the internship
- 2Meet the program requirements
- 3Complete all required evaluations
- 4Credential issued with a permanent ID
- 5Any employer can verify it
4 programs accepting applications
Data Migration Engineer
Data Engineering
Move data between systems without losing, corrupting or silently duplicating any of it. Over 12 weeks you'll plan and execute migrations end to end: profiling the source, mapping schemas, handling the rows that do not fit, reconciling counts, and designing a rollback you would actually be willing to use. Migrations are judged entirely on what does not happen. The work is in the verification — proving that what arrived matches what left, and knowing what to do about the three percent that never maps cleanly. Topics covered: source profiling and data quality assessment, schema mapping and type coercion, incremental and dual-write cutover strategies, idempotent and resumable loads, reconciliation and row-level verification, rollback planning, zero-downtime cutover.
View detailsData Analyst
Data Analytics
Turn raw data into decisions a product team will actually act on. Over 10 weeks you'll work with real business datasets, write production-quality SQL, build dashboards stakeholders will use weekly, and present findings to product and engineering owners. The work is half technical, half communication. You'll learn the SQL patterns that scale, the BI conventions that prevent stale numbers, and the storytelling structure that gets your insight onto the roadmap. Topics covered: advanced SQL, exploratory data analysis, dashboarding (Metabase / Looker patterns), A/B test analysis, cohort and funnel analysis, executive communication, statistical fundamentals.
View detailsData Engineer
Data Engineering
Build the data pipelines that every analyst, ML engineer, and product team depends on. Over 12 weeks you'll design and ship ETL/ELT flows, work with cloud data warehouses, handle schema evolution gracefully, and operate both batch and streaming workloads on real datasets. By the end you'll be comfortable writing dbt models a senior engineer would merge without hesitation, debugging Airflow DAGs at 2 a.m., and explaining why your warehouse bill went up this month. Topics covered: ETL/ELT design, SQL fluency, dbt, Apache Airflow, data warehousing (Snowflake / BigQuery / Redshift patterns), streaming basics (Kafka), data quality testing, cost optimisation.
View detailsFor students
Work you can show.
Complete weekly engineering sprints, get evaluated on each, and leave with deliverables and a credential. Free at every stage, wherever you are.
For universities
A structured industry pathway.
A verifiable internship your students can complete alongside coursework, with a published rubric you can inspect.
For industry experts
Mentor where it matters.
Shape curriculum and take escalated reviews on your own schedule, without the weekly grading load.
Start building the evidence.
Pick a program, apply in a few minutes, and start week one as soon as you're approved. Free for students, worldwide.