Job description
Description
🤓 TripleTen is an EdTech company that designs and runs tech career learning programs for the US and Latin American markets. We've been doing it for over five years, teaching complete beginners — people with no prior tech background — through cohort-based programs built on our own platform and curriculum, developed in partnership with Nebius AI. Our team is fully remote and globally distributed, and we serve a large, active student base across both regions.
We're looking for the person who brings an AI and Machine Learning curriculum to life for students: hosts the live sessions, reviews the work, runs the model and system review boards, and sets the technical bar for the people supporting alongside them. The curriculum is built by a team of senior authors.
This is a teaching and reviewing role. You'll be the senior technical presence students learn from week to week. You'll run live sessions and office hours, give real engineering feedback on student ML deliverables, and act as the escalation point for a team of supporting instructors who handle first-line questions. The best person for this is someone who has actually built and shipped AI systems in production, has opinions about what a sound ML system looks like, and can tell an experienced engineer why their approach is wrong.
Your audience: An experienced developer or engineer – SWE, data engineer, data analyst, DevOps/SRE, or quant – who wants to move into an adjacent AI/ML role (MLE, AI engineer, MLOps, data scientist) and carry their existing experience across, not start over.
Format: Live sessions, office hours, 1:1s, and workshops timed around US Eastern Time – mostly 2:00 PM to 9:00 PM ET. Estimated 10 to 15 hrs/week.
Please submit all resumes or CVs in English.
What you will do
Host live sessions focused on the design of ML systems: agentic architectures, orchestration, evaluation, and reliability of LLM-based systems.
Run group office hours, 1:1 sessions and tech mock interviews for students working through projects.
Review student projects against rubrics.
Set the technical standard for a team of supporting instructors who cover questions and first-line review, and act as their escalation point.
Requirements
7+ years of professional ML engineering experience, currently working at senior level or above (Senior/Lead/Staff ML Engineer, AI Engineer, or ML/AI Systems Architect).
Has built and shipped AI or ML systems that ran in production in a real company, not just notebooks or side projects.
Depth across the AI engineering spine: agentic systems (L3, LangChain/CrewAI/ADK frameworks, orchestration, self-correction), agent reliability and guardrails, MCP; LLM evals (eval harness, LLM-as-judge, hallucination metrics), applied fine-tuning (SFT/LoRA); AI coding tools in the SDLC, LLM observability, A/B experiment design.
Can explain why a modeling or architecture decision was made, not just how it was implemented, and can diagnose and critique someone else's work live.
Strong technical communication: comfortable leading a live session and writing clear, specific review feedback.
Strong English C1+ — instruction and review are in English for a US-based audience.
Comfortable using AI tools in day-to-day technical work.
Preferred experience:
Has taught, mentored, or run technical sessions before: internal tech talks, onboarding, mentoring engineers, bootcamp or workshop instruction.
Production experience with agentic frameworks (LangChain, CrewAI, ADK) and MCP-based tool integrations.
Hands-on ownership of an eval or observability stack in production, not just usage.
Work experience at a recognizable company.
Familiarity with online education platforms or running programs.
What we can offer you
Fully remote work, with live sessions scheduled within US afternoon and evening hours.
A digital office: we use modern tools (Notion, Slack, Zoom) to keep collaboration smooth.
Professional trust and autonomy: no micromanaging.
A diverse, international, close-knit team excited to work with you!
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.