AI Systems & ML Engineering Industry Expert

TripleTen is an EdTech company that designs and runs tech career learning programs for the US and Latin American markets. Weve 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.

Were launching three advanced engineering programs for working mid/senior engineers and were looking for a small number of Industry Experts to set the technical bar in each of them.

This is not a teaching or content-authoring role. The curriculum is built by a separate team of senior authors. What we need from you is judgment: the kind of call a Staff or Principal engineer makes when they look at a design and know in thirty seconds that the service split is wrong the eval is measuring the wrong thing or the scope will not survive contact with a client.

Our students design and defend real systems. Your role is to challenge those decisions the way youd challenge a peers and to be the name that tells an experienced engineer this program is worth their time.

The audience

Working mid- and senior-level engineers typically 510 years in: backend platform ML and infrastructure people with senior and staff titles and the occasional engineering manager in the room. Many write production code daily. They are not career changers and they spot shallow feedback instantly. The bar here is real seniority not familiarity with the topic.

• Sit on final project defenses. Review a deployed system a distributed-systems capstone an agentic architecture or a client-facing delivery package against the rubric then run the defense and give structured senior-level critique.
• Chair mock review boards and executive-panel presentations. Architecture review boards model and system reviews exec go/no-go presentations depending on the program.
• Host one or two live sessions a month on the design and decision layer of your domain: where systems split how they fail which tradeoff to make and why.
• Set the technical standard for the instructors running weekly delivery and act as their escalation point on the hard design calls.

You are not on the hook for weekly coverage office hours rotations or first-line questions. A separate team handles that.

• Your name and profile featured as an Industry Expert on the program page.
• A peer-level audience. A defense is a technical review with a working engineer not homework grading.
• A genuinely small commitment. 410 hours a month slots booked about two weeks ahead pausable at any time.
• Hourly payment negotiable depending on experience track and scope.
• Fully remote with a small international team and no micromanaging.

Requirements:

• 8 years of professional engineering experience currently at senior/staff/principal level or equivalent (Staff/Principal Engineer Senior/Staff ML Engineer Solutions Architect Forward Deployed Engineer technical lead).
• Youve shipped systems that run in production at real scale as an employee in an engineering role not coursework not side projects not a slide deck about someone elses platform.
• You can explain why a decision was made not just how it was implemented and diagnose and critique someone elses architecture live on a call without preparation.
• A public technical footprint: GitHub conference talks a book or OReilly/Manning title a technical blog open-source work or documented mentorship.
• Strong English (C1). Sessions and written reviews are in English for a US-based audience.
• Time zone: Americas strongly preferred (US / Canada / LatAm). Defenses are booked in advance so some flexibility exists but sessions land in US afternoon and evening hours.
• Comfortable using AI tools in day-to-day technical work.

Domain depth one of three tracks

You dont need all three. Tell us which one is yours.

AI/ML Engineering. Agentic systems and orchestration (LangChain LangGraph CrewAI ADK) agent reliability and guardrails MCP; LLM evals eval harnesses LLM-as-judge hallucination metrics; applied fine-tuning (SFT/LoRA/PEFT); LLM observability A/B experiment design model serving and inference cost.

AI Systems Engineering. System and API design service architecture cloud and infrastructure (AWS Kubernetes Terraform CI/CD) distributed systems observability and incident response plus LLM-powered systems in production: RAG model serving fallback paths cost control.

Forward Deployed Engineering. End-to-end ownership of deployments in real client or enterprise environments: discovery and scoping under ambiguity stakeholder management without formal authority integration with enterprise systems rollout and adoption on top of LLM and agent systems in production RAG over enterprise data and APIs/integrations.

Nice to have

• Youve already run technical sessions in some form: internal tech talks conference workshops engineer onboarding or mentoring.
• Hands-on ownership of an eval or observability stack in production not just usage of one.
• Experience being the primary technical resource embedded with a customer team (for the FDE track).