Senior AI Systems & ML Engineering Industry Expert

🤓 TripleTen is a career learning platform for tech professionals and complete beginners ready to move into higher-paying tech and AI roles. We launched in 2020, we run programs across the US and Latin America, and 7,500+ people worldwide have completed one. Our team is fully remote and globally distributed.

In 2026 we opened a second tier of programs for people already working in tech: AI Systems Engineering, AI & Machine Learning, and Forward Deployed Engineering. Same platform, different bar.

We're launching three advanced engineering programs for working mid/senior engineers, and we're 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 you'd challenge a peer's — and to be the name that tells an experienced engineer this program is worth their time.

Each program is a chain of five production-level projects, and every project ends in a live defense.

• 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.

Who you'd be reviewing

Working engineers and tech professionals, not complete beginners. Middle or senior developers, platform and data engineers, network and infrastructure people, security and incident-response specialists. A few are between jobs and moving fast. Most have hit a ceiling where they are and want the next step: designing and owning production AI systems instead of shipping features around them.

Most of them study around a full-time job, they opened your GitHub before the syllabus, and they can tell rehearsed feedback from the real thing.

• 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).
• You've 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 else's platform.
• You can explain why a decision was made, not just how it was implemented — and diagnose and critique someone else's architecture live, on a call, without preparation.
• A public technical footprint: GitHub, conference talks, a book or O'Reilly/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 don't 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

• You've 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).

• Your name and profile featured as an Industry Expert on the program page.
• A network of engineers from other companies. The other instructors/industry experts come from engineering teams US engineers recognize, and you'll be working alongside them.
• First look at senior talent. You watch experienced engineers defend real systems under pressure, so you leave the cohort knowing who you'd hire.
• Personal brand, with proof behind it. Your profile on the program page, plus an Industry Expert line for your own bio and talks. It's also the kind of external technical credit that counts in a promotion packet or an O-1 petition.
• A genuinely small commitment. 4–10 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.