Deep Machine Learning Intern (ML Engineering + SWE)

About the Role We are looking for a rare blend of deep technical knowledge and the ability to communicate it well. This role sits at the intersection of an ML Engineer and a Deep Learning Data Scientist — someone who understands the mathematics and algorithmics behind modern deep learning systems, pipelines, agents and can work hands-on with code, cloud infrastructure, and can translate that expertise into structured, high-quality learning experiences.

What You'll Do

• You must have a state of AI understanding of Deep Machine Learning, MLOps, LLMs, NLP Foundations, Agentic AI, and Generative AI.
• Mathematically deduce Deep Learning Algorithms into simple knowledge bytes and lessons.
• Build, train, and deploy end-to-end ML pipelines on AWS, capable of demonstrating real-world deep learning workflows to users.
• Work with and explain the algorithmics of modern deep learning architectures — Transformers, LLMs, GANs, and recent advances including RAG, LoRA, AI Agents, and Agentic AI systems.
• Curate and validate technical content for accuracy, depth, and pedagogical clarity across beginner, intermediate, and advanced learner levels.
• Develop and maintain code repositories on GitHub that serve as reference implementations and lab environments for course participants.
• Review, sequence, and structure curriculum modules to ensure a coherent and well-scaffolded learner journey.
• Stay current with the latest research and tooling in deep learning, and continuously incorporate relevant advancements into course content.
• Regularly guide learners through complex technical concepts through regular live or recorded sessions, making sophisticated ideas approachable without oversimplifying them.

What We're Looking For Must-haves

• Deep expertise in the mathematical algorithmics of deep learning — including the mechanics of neural architectures, optimisation theory, attention mechanisms, and probabilistic foundations.
• Strong command of modern deep learning systems: Transformers, LLMs, GANs, diffusion models, RAG pipelines, LoRA fine-tuning, and agentic frameworks.
• Advanced Python programming skills — clean, well-documented, production-aware code.
• Hands-on experience with at least one major cloud platform (AWS preferred; GCP or Azure also considered), including compute, storage, and ML service offerings.
• Proficiency with GitHub for version control, collaboration, and repository management.
• Comfort working in Linux environments.
• Excellent written and spoken English — able to explain complex technical ideas with precision and clarity to a range of audiences.
• A genuine passion for teaching and helping others build deep technical understanding.

Nice-to-haves

• Hands-on experience with frameworks such as Hugging Face Transformers, LangChain, LlamaIndex, PyTorch, or JAX.
• Familiarity with MLOps practices and tools — experiment tracking, model versioning, CI/CD for ML, and pipeline orchestration.
• Prior experience creating technical content — tutorials, notebooks, documentation, course material, or technical blog posts.
• Exposure to instructional design principles or experience in teaching, tutoring, or mentoring in a technical subject.
• Awareness of leading AI learning platforms and curricula (fast.ai, d2l.ai, DeepLearning.AI, Hugging Face courses, etc.).

Tech Stack

• Advanced Python Programming
• AWS
• Docker
• GitHub
• Linux/ Shell Scripting
• MLFLow What We Offer
• A central role in building deep learning curricula used by real learners and professionals.
• Mentorship from senior AI researchers, engineers, and learning designers.
• Hands-on access to cloud compute resources for experimentation and content development.
• Opportunity to publish technical content, maintain open-source repositories, and build a visible body of work.
• Flexible hours and an async-first work culture.
• Pre-placement offer (PPO) consideration for outstanding performers.
• Paid a fixed monthly stipend and Performance based incentive

How to Apply Submit your resume, a link to your GitHub profile or any technical work you have published, and a brief note on one deep learning concept you find commonly misunderstood and how you would teach it differently. Shortlisted candidates will be invited for a 20-minute technical conversation.

JOB ID: DML-SWE-MLE-JUN26