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REMOTE Technical Curriculum Developer, AI Programming (Software Engineering Audience)

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CONTRACT DETAILS

1099 or C2C (No W2)

W2 employment is not available for this engagement.

• 6-month contract (strong likelihood of extension)
• 100% Remote (U.S. or Canadian candidates only)
• Start date: September 2026
• 40 hours per week @ $55–$65/hour (C2C or 1099), based on experience
• Standard U.S. business hours (EST/CST/MST/PST)
• Heavy Slack collaboration + Laptop provided
• Portfolio REQUIRED

THE ROLE

About the Role

This is an Individual Contributor (IC) curriculum development and technical advisory role supporting a Global Tech training team. The training you'll support is built for software engineers — full-stack, front-end, and back-end developers — so you need to be one of them. We're looking for a Technical Curriculum Developer with a real software engineering background and deep AI expertise, who can review AI programming content the way the target audience will actually consume it, catch what's missing or wrong, and improve how that content gets built.

You'll serve as the deep-expertise anchor for our client's AI programming curriculum. You won't be building courses end-to-end so much as making them right — evaluating curricula for technical accuracy from a working developer's perspective, filling content gaps, surfacing the real-world context that turns generic AI material into training engineers trust, and implementing better processes to speed up the design and development phases.

You'll embed with engineering teams and partner directly with platform owners, architects, developer advocates, L&D team members, and technical writers — lending the technical judgment they need and translating complex AI concepts into something engineers can learn and apply. If you also bring AI learning-enablement experience, that's a significant bonus.

• Review Content as a Developer: Evaluate AI programming curricula for technical accuracy, depth, and relevance — from the perspective of the full-stack, front-end, and back-end engineers the training is built for. Act as the trusted technical voice ensuring content reflects how AI systems and real engineering work actually fit together.

• Find the Gaps: Use your software engineering background to spot what's missing, outdated, oversimplified, or misleading — the gaps a non-engineer reviewer would miss — and flag or close them.

• Fill Content Gaps: When a missing or thin area is identified, develop the technical content to close it — written guides, video tutorials, hands-on labs, technical demos, code examples, and reference material — grounded in real engineering use and delivered for both self-paced and instructor-led formats.

• Improve How Content Gets Built: Identify and implement new processes, tooling, and AI-assisted workflows to speed up the design and development phases and raise the quality bar across the team.

• Iterate on Feedback: Review feedback from learners, SMEs, and engineering partners, then take the steps to correct errors, improve clarity, and keep content quality high as platforms and tools change.

• Provide Context & Intuition: Help the training team understand and surface the underlying context, edge cases, and practitioner intuition that should be embedded in training but is easy to miss without deep hands-on experience.

• Translate Complexity: Make sophisticated AI/ML concepts accessible to a technical audience without oversimplifying or losing rigor.

• Embed with Engineering Teams: Act as an early tester and end-user by embedding within engineering teams. Collaborate directly with platform owners and architects to understand new technologies from the developer's perspective, then carry that understanding into the training.

• Collaborate Across Teams: Work alongside developer advocates, L&D team members, and technical writers to develop and implement a holistic curriculum, keeping it technically sound and current as the field moves.

• 4+ years of hands-on software engineering experience — full-stack web development or general software engineering (front-end and back-end). You've shipped real code. This is a hard requirement, because you'll be reviewing content for the same audience.

• Deep, hands-on technical expertise in AI/ML — e.g., machine learning, deep learning, LLMs, data science, or applied AI engineering. Equally core to the role.

• Hands-on with a modern AI/ML stack — proficient programming and debugging in Python (and SQL where relevant), familiarity with common frameworks and tooling (e.g., PyTorch, TensorFlow, or LLM/agent frameworks), standard software development lifecycle practices (Git, testing, modularization), and at least one major cloud provider (AWS, Azure, or GCP).

• Demonstrated ability to evaluate technical content for accuracy and to identify what's missing, outdated, or misleading from an engineer's point of view.

• Able to learn new technologies and platforms quickly, and comfortable making data-driven decisions about what content needs to change.

• Experience implementing or improving processes, tooling, or AI-assisted workflows to make content development faster and better.

• Experience explaining or teaching technical concepts to engineering audiences — through mentorship, internal enablement, talks, documentation, writing, or formal training.

• Comfort working in fast, ambiguous, AI-assisted environments and collaborating heavily over Slack.

• Strong written communication; able to produce clear technical content, including code examples, when gaps need filling.

• Prior experience as an AI Learning & Enablement expert — someone who can not only advise on technical accuracy but also help shape how AI concepts get taught and adopted at scale.

• Background in instructional design or corporate technical training.

• Familiarity with e-Learning tools (e.g., Articulate Storyline, Rise) and Learning Management Systems (LMS).

• Experience embedding with engineering or platform teams as a technical advisor.

• You've shipped real code and you have genuine depth in AI/ML — and you enjoy making complex ideas land for other engineers.

• You want your expertise to shape how an entire engineering org learns AI — measured by the quality and trustworthiness of the training, not by course counts.

• You like being the person teams turn to when they need to know whether the content is actually right, and you can't help improving the process while you're at it.

• You're energized by ambiguity, fast cycles, and AI-assisted ways of working.