You already ship production code. Step into an AI/ML role in less than a year. Build Python depth, learn the math under the models, and add MLOps and agentic systems.
Start your skills auditNot for beginners. A skill check is required before signing up.
TripleTen brings the training expertise. Nebius Group brings the AI infrastructure leadership—as a core partner of Meta, NVIDIA, and Microsoft.
Project 1 hands you structured inputs and hints. By Project 5, you'll get a business request and choose the tools and approach, start to finish. Every project is based on real-life business problems.
Get 1:1 mentorship, coaching, and guidance from Tier-1 architects and Staff engineers at top tech companies.
Mentor students in TripleTen’s programs for tech beginners, and build senior-level leadership skills.
Practice technical interviews with working ML engineers, and hone your HR interviews, too. Career coaches help position your resume and LinkedIn for your target roles.
Software, backend, and data engineersYou ship production code every day and you’re new to ML. Add the modeling, evaluation, and deployment layer on top of the experience you already have.
DevOps, SRE, QE, and analytics engineersBring your DevOps expertise to ML systems, or turn years of data work into a modeling role. Your existing domain knowledge is an asset for hiring managers.
Senior engineers adding AI/ML to level upYou're not looking to pivot, or even leave your company. But you're looking to gain AI/ML competence.
Not for beginners. If you’re starting from zero, TripleTen’s entry-level programs are the right fit.
Instructors at Big Tech and Fortune 500 know how to succeed and what hiring managers seek.
Day 1One engineer mentors you one-on-one, for the entire program.
While you buildReview the architecture together — how they solved it at work, and where your approach is likely to break.
ReviewDefend your design decisions to a staff-level engineer, the same way you would to a hiring manager.
Instructors cover orchestration, guardrails, and tool integration, including what it takes to keep an agent running after launch.
Learn how to test a model before it reaches customers. Build eval harnesses and use one model to check the output of another.
Fine-tune when a prompt isn't enough. Monitor when the model drifts.
One-on-one mentorshipWeekly sessions with a working ML engineer to work through project feedback.
Career guidanceGraduate with a career roadmap, a resume & LinkedIn profile that get attention, and a target company list.
Alumni networkConnect to a network of 7,500+ alumni worldwide.
Partner with Nebius Group, Big Tech engineers, and TripleTen alumni to scale your career.
Big Tech-reviewedLevel up, with proofGet your architecture audited by leading engineers. Graduate with references.
Active networkGet noticedOur job board features 300+ daily tech opportunities from 250+ partner companies.
Top-tier standardsPass the $200K+ barLearn the exact tradeoffs tested by OpenAI, Uber, and Stripe.
How Education Is Changing In The AI Era
TripleTen grad Jeremy Rivera traded night shifts in a warehouse for a remote developer job.
Survey: How office workers are really using AI on the job
TripleTen is the overall best Software Engineering program for 2024.
We'll help you create tailored job search materials:
Drill with Tier-1 hiring managers to hone your performance and fix blind spots.
Support beginner students in other TripleTen programs, mentor them, and build leadership experience—the kind that gets you into senior and staff-level roles.
100% student-owned reposDeployed
Eval-gated5 projectsTrain and evaluate a model on messy data: duplicates, leakage, missing values, schema drift.
Ship a model behind an API—containerized, logged, and monitored, with the retraining path written down.
Build a RAG pipeline with retrieval quality you can measure, then wrap it in an agent with tool use and guardrails.
Add an evaluation harness that blocks a merge when quality drops — checked against offline metrics, online behavior, and an LLM-as-a-judge.
Take an end-to-end ML system to a production Definition of Done, reviewed against industry standards by a staff-level engineer.
Book a call to talk to a career mentor, get your questions answered, and reserve your spot.
Five projects → one production system
35% theory / 65% hands-on
For engineers with 2+ years in production
AI-assistedProject first, theory when you need it.
The accelerator offers 250 hours of optional theory to support your main projects.
Engineers review this in production.
And your final system will be evaluated by a staff-level engineer.
You keep everything.
Your repositories hold everything: deployed models, tests, and notes on decisions.
“Here’s the product requirement. Turn it into something that ships.”
Tools used
Theory behind it
What we hand you
A PRD, a data source behind an API, and stakeholders who will change their minds.
What you decide yourself
The ML spec. You write it, and you answer for the scope you agreed to.
What the review checks
Whether it does what the spec said.
“It works on your laptop. Now put it in front of real traffic.”
Tools used
Theory behind it
What we hand you
The goal and the service-level targets.
What you decide yourself
The architecture.
What the review checks
Why you chose this architecture and not another one.
“Write the patient summary the way our doctors write it.”
Tools used
Theory behind it
What we hand you
The task and the domain.
What you decide yourself
The definition of good. You set the quality bar and prove you hit it.
What the review checks
The bar you set, and your evidence you cleared it.
“Our support queue is drowning. Build something that can close a ticket without being talked into anything.”
Tools used
Theory behind it
What we hand you
The queue, a knowledge base, and connected tools.
What you decide yourself
The list of ways it can fail. You find those yourself, then you attack your own agent to prove it holds.
What the review checks
Whether your agent survives being attacked. This is also the first project where you review another student’s work.
“We want to sell this as a product. Build it and ship it.”
Tools used
Theory behind it
What we hand you
A description of the business and the customers it serves.
What you decide yourself
Everything: the stack, the approach, and the metrics of success.
What the review checks
A staff-level engineer reviews the whole product and asks you to explain every decision you made. This is the project you show employers.
Access advanced ML, AI, and Cybersecurity modules on demand.
Backed by Nebius Group—a core technology partner of Meta, NVIDIA, and Microsoft. Across the group, 220+ senior engineers are building large-scale AI infrastructure, including global cloud platforms and hyperscale GPU clusters.
We know exactly what the market demands. To bridge the talent gap, we rely on TripleTen's proven training expertise and employer network, trusted by over 7,500 graduates across North America and Latin America.
Pay in a single installment, or up to 4 payments. You can withdraw with a 100% refund in your first 2 weeks.
Apply for financing with one of our partners, and learn if you are eligible in minutes. Soft credit check req’d.
$1,000 deposit. No credit check req’d, 0% interest. 6, 12, or 24-month plans available.
Employer reimbursement documentation provided on request. You can withdraw with a full refund in your first 2 weeks.
This will: instructors who are working ML engineers, projects that culminate in a deployed system, and a defensible portfolio.
