Software Developer Jobs

About The Position

Requirements

• 4+ years of hands-on software engineering experience (full-stack web development or general software engineering - front-end and back-end).
• Deep, hands-on technical expertise in AI/ML (e.g., machine learning, deep learning, LLMs, data science, or applied AI engineering).
• Proficient programming and debugging in Python (and SQL where relevant).
• Familiarity with common AI/ML frameworks and tooling (e.g., PyTorch, TensorFlow, or LLM/agent frameworks).
• Familiarity with standard software development lifecycle practices (Git, testing, modularization).
• Experience with at least one major cloud provider (AWS, Azure, or GCP).
• Demonstrated ability to evaluate technical content for accuracy and identify what's missing, outdated, or misleading from an engineer's point of view.
• Ability to learn new technologies and platforms quickly.
• Comfortable making data-driven decisions about content changes.
• Experience implementing or improving processes, tooling, or AI-assisted workflows for faster and better content development.
• Experience explaining or teaching technical concepts to engineering audiences (mentorship, internal enablement, talks, documentation, writing, or formal training).
• Comfort working in fast, ambiguous, AI-assisted environments.
• Comfort collaborating heavily over Slack.
• Strong written communication skills; able to produce clear technical content, including code examples.

Nice To Haves

• Prior experience as an AI Learning & Enablement expert.
• 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.

Responsibilities

• Evaluate AI programming curricula for technical accuracy, depth, and relevance from the perspective of full-stack, front-end, and back-end engineers.
• Act as the trusted technical voice ensuring content reflects how AI systems and real engineering work actually fit together.
• Identify missing, outdated, oversimplified, or misleading information in AI programming content.
• Develop technical content to fill identified gaps, including written guides, video tutorials, hands-on labs, technical demos, code examples, and reference material.
• Identify and implement new processes, tooling, and AI-assisted workflows to speed up content design and development phases and improve quality.
• Review feedback from learners, SMEs, and engineering partners to correct errors, improve clarity, and maintain content quality.
• Provide underlying context, edge cases, and practitioner intuition to training content.
• Make sophisticated AI/ML concepts accessible to a technical audience without oversimplifying or losing rigor.
• Embed within engineering teams to act as an early tester and understand new technologies from a developer's perspective.
• Collaborate with developer advocates, L&D team members, and technical writers to develop and implement a holistic curriculum.

Benefits

• Laptop provided