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Full Time Ai Tutor Jobs (NOW HIRING)

... AI coding tools running alongside you. * Building real LLM-powered product features: tutoring ... full-time production engineering experience shipping software to real users at real scale. * At ...

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How much do full time ai tutor jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for full time ai tutor in the United States is $20.22, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $24.04 per hour, depending on experience, location, and employer.

What are Full Time AI Tutors?

Full Time AI Tutors are professionals who provide ongoing support, instruction, and guidance to students or employees learning about artificial intelligence concepts, tools, and applications. They may work in educational institutions, online platforms, or corporate training environments, helping learners understand everything from basic AI principles to advanced machine learning techniques. Their role typically involves creating lesson plans, answering questions, reviewing assignments, and offering personalized feedback to facilitate effective learning. As full-time employees, they dedicate their working hours to supporting learners and staying updated with the latest developments in AI.

What are some common challenges faced by Full Time AI Tutors when supporting diverse learners?

Full Time AI Tutors often encounter the challenge of addressing a wide range of student backgrounds, learning styles, and proficiency levels. Adapting explanations and providing personalized feedback to ensure all students grasp complex AI concepts can be demanding, especially in large or remote settings. Additionally, staying current with rapidly evolving AI technologies and integrating them effectively into teaching materials requires continuous learning and flexibility. Collaborating closely with other educators and support staff can help overcome these challenges and promote a positive learning environment.

What are the key skills and qualifications needed to thrive as a Full Time AI Tutor, and why are they important?

To thrive as a Full Time AI Tutor, you need a solid understanding of artificial intelligence concepts, programming languages like Python, and a relevant educational background in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), online learning platforms, and possibly teaching certifications are typically required. Strong communication, patience, and the ability to tailor complex topics to diverse learning styles are critical soft skills. These skills ensure effective knowledge transfer, student engagement, and successful learning outcomes in a rapidly evolving technical field.
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What cities are hiring for Full Time Ai Tutor jobs? Cities with the most Full Time Ai Tutor job openings:
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Infographic showing various Full Time Ai Tutor job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 96% Full Time, 2% Part Time, and 1% Temporary. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $42,053 per year, or $20.2 per hour.

Full-time

Posted 10 days ago


Job description

Software Engineer (AI-Forward)
As a Software Engineer with Texas Sports Academy, you play a key role in building the software that runs our school - student records, academic mastery tracking, training data, parent portals, admissions, and the AI-powered tools our guides and coaches use every day. This is a senior, AI-forward seat. You work directly with the founders, ship code every week with AI in your loop, and own the LLM-powered features that make our school feel nothing like a traditional school.
What you will be doing
  • Designing, building, and shipping product features across the full stack every week, with AI coding tools running alongside you.
  • Building real LLM-powered product features: tutoring agents, parent-facing copilots, coach-facing dashboards, retrieval over student data, evals for every one of them.
  • Working directly with the founders on scope, architecture, and trade-offs - no PM layer in between.
  • Owning the reliability, security, and cost of both classical systems and LLM pipelines.
  • Running your own AI coding workflow - prompts, subagents, custom tools, MCP servers - and raising the bar on how the whole team uses AI.
  • Writing evals and regression tests for AI features the same way you'd write unit tests for classical code.

What you will NOT be doing
  • Pretending AI is optional - if you're not already coding with Claude Code, Cursor, Codex, or an equivalent agent loop every day, this role is not for you.
  • Writing tickets for junior engineers to implement.
  • Sitting in status meetings all day - few meetings, more shipping.
  • Working on a narrow slice of a giant codebase.

Key responsibilities
  • Ship production code and AI features real students, parents, and staff rely on every day.
  • Own at least one major system end-to-end.
  • Move fast - features go from idea to production in days, not quarters - without breaking things.
  • Raise the AI-engineering bar as the team grows.

Requirements
  • Bachelor's or master's degree in Computer Science, Engineering, Math, or Physics.
  • 6+ years of full-time production engineering experience shipping software to real users at real scale.
  • At least one prior senior, staff, or tech-lead role.
  • Daily, fluent use of AI coding tools (Claude Code, Cursor, Codex, Windsurf, Aider, or equivalent) as your default way of writing software.
  • At least one shipped LLM-powered feature in production, with an eval story.
  • Strong skills in a modern web stack (TypeScript / React / Node or Python / Postgres / AWS or GCP).
  • Excellent written English
  • Legal authorization to work from India or Pakistan as a remote contractor with a US company.

Nice to have
  • Master's or PhD from a tier-1 institution.
  • Former founder, first-five engineer at a funded startup, or shipped products that reached real users at scale.
  • Agent frameworks (LangGraph, CrewAI, Mastra, custom), vector search / RAG at scale, evals (Braintrust, LangSmith, custom), prompt caching, MCP servers, structured output / tool-use reliability, voice agents.
  • Public GitHub or a personal AI project we can actually try.
  • A personal project you built because you wanted to.