AI Training & Enablement Lead

Giga is hiring a full-time AI Training & Enablement Lead to build the learning and adoption system that keeps the company at the front edge of applied AI. You will help employees move from awareness to confident, responsible use and from one-off experiments to workflows that hold up in real operations.

Think internal AI creator meets adoption operator. You should be able to hold a room, record a sharp tutorial, sit beside an engineer, recruiter, finance leader, or project operator, and turn a messy work problem into a clear learning experience and a better way of working.

This is not a traditional learning-and-development role and it is not a generic AI-evangelist role. You will own the human capability layer while partnering with Business Systems & Automation, Digital Operations, Security, Legal, People, and functional leaders on tools, workflows, controls, and rollout.

This is a full-time role. Austin, Texas preferred.

• Own Giga's company-wide AI learning and enablement roadmap, with practical paths for employees, managers, leaders, power users, and builders.
• Learn how work actually happens across engineering, manufacturing, project delivery, finance, sales, recruiting, People, and other functions, then turn the highest-value needs into role-specific learning.
• Design and deliver hands-on workshops, workflow labs, onboarding sessions, office hours, and team coaching that help people use AI in real work.
• Act as Giga's internal AI creator: produce concise, high-signal videos, live demos, field notes, job aids, and reusable examples that employees want to watch and use.
• Build and maintain an easy-to-navigate enablement hub with approved-tool guidance, workflow playbooks, prompt and agent patterns, verification checklists, and examples from Giga teams.
• Build a network of AI champions who adapt central guidance to their functions, coach peers, surface friction, and share what works.
• Turn repeated employee questions and workflow requests into reusable training, templates, controlled experiments, or clearly scoped briefs for Business Systems & Automation and other builders.
• Maintain a practical frontier radar: test meaningful new models, agents, and workflow patterns; explain what changed; recommend what Giga should pilot, scale, defer, or stop.
• Partner with Digital Operations, Security, Legal, People, and data owners so every learning experience includes clear guidance on approved tools, confidentiality, output verification, human review, and escalation.
• Measure adoption beyond attendance by tracking activation, repeat use, workflow reuse, confidence, blocker closure, quality, cycle time, hours freed for higher-value work, and responsible-use signals.
• Capture internal success stories and adoption barriers, then give leaders concise evidence and recommendations on where to invest, intervene, or change course.
• Create protected moments for learning and experimentation, including showcases, build sprints, or function-specific discovery sessions when they are the best way to move from curiosity to useful work.

• Giga employees share a practical baseline for using approved AI tools responsibly and know where human judgment is required.
• Teams can point to role-specific AI workflows that are used repeatedly in real work, not just demonstrated in training.
• Giga has a current, trusted library of short-form learning, playbooks, examples, and reusable workflow assets.
• A functioning champion and feedback network turns frontline questions into better guidance, better workflows, and better product decisions.
• Leadership can see credible evidence of adoption, business value, friction, and risk rather than relying on logins, course completions, or anecdotes alone.
• Giga develops the habit of evaluating frontier AI quickly, using it safely, and scaling only what improves speed, quality, judgment, or team capacity.

• A portfolio that shows you can teach complex technology clearly through live facilitation, video, writing, job aids, or a combination of formats.
• Hands-on fluency across modern AI tools and workflows, with a specific example of an agent, automation, reusable workflow, or other capability you personally built or configured.
• Evidence that you changed behavior and sustained adoption, not just delivered a launch, a keynote, or a course.
• The ability to diagnose a real business workflow, find the point where AI can help, and explain when deterministic automation or human judgment is the better answer.
• Exceptional facilitation and storytelling across audiences with very different levels of technical confidence, from frontline operators to executives.
• Strong content judgment: you can make a useful tutorial quickly, keep it accurate, and retire or revise it when the underlying tool changes.
• A record of measuring what changed after enablement, including usage, workflow adoption, quality, cycle time, employee hours freed, or another meaningful operating result.
• Practical judgment about confidentiality, permissions, hallucinations, grounding, output verification, human review, and when AI should not be used.
• High ownership, curiosity, direct communication, and comfort operating quickly when the technology and the organization are changing at the same time.

• You have built an audience or learning community through YouTube, a course, a newsletter, workshops, or another public body of work; reach matters less than the quality and usefulness of the teaching.
• Experience in energy, infrastructure, data centers, manufacturing, construction, engineering, or another physical operating environment.
• Experience in developer education, customer education, solutions consulting, technical enablement, change leadership, or adult learning.
• Comfort recording and editing concise screen-based tutorials, demonstrations, and internal video series.
• Enough technical depth to prototype with agents, APIs, low-code tools, or code and to work credibly with software builders without pretending to own production architecture.
• Experience building champion networks, communities of practice, academies, hackathons, or other peer-led adoption programs.

• Share one example of how you teach. It can be a short video, a live-workshop clip, an article, a tutorial, a course module, a deck, or another learner artifact.
• Show one AI workflow you built or helped a team adopt. Explain the prior process, what changed, and how you knew it worked.
• Audience size and production polish are not the point. We care about clarity, practical judgment, credibility, and whether people used what you taught.