AI Systems & ML Engineering Industry Expert

🤓 Tr ipleTen 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.

• 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).