Job Overview
We are seeking a brilliant and research-oriented Deep Learning Lecturer to lead our advanced AI curriculum. You will be responsible for demystifying neural network architectures, optimization algorithms, and the latest breakthroughs in machine intelligence. Your goal is to guide students from theoretical mathematical foundations to the hands-on engineering of state-of-the-art models, preparing them to tackle the most challenging problems in computer vision, natural language processing, and generative AI.

Job Responsibilities
Instructional Delivery: Lead courses on advanced Deep Learning topics, including Convolutional Neural Networks (CNNs), Transformers, Recurrent Neural Networks (RNNs), Attention Mechanisms, Diffusion Models, and Reinforcement Learning.

Curriculum Development: Design a rigorous curriculum that balances foundational mathematics (linear algebra, calculus, probability) with modern implementation using frameworks like PyTorch, TensorFlow, or JAX.

Hands-on Engineering: Oversee coding laboratories where students build, train, and fine-tune models on high-performance GPU clusters, focusing on hardware optimization and efficiency.

Research & Mentorship: Drive original research in deep learning, publish in leading venues (e.g., NeurIPS, ICML, ICLR), and mentor PhD/Master’s students on thesis work and capstone projects.

Assessment & Project Oversight: Create challenging assessments that evaluate not just coding ability, but also a student's capacity to diagnose model failure modes, optimize training loops, and interpret performance metrics.

AI Ethics & Safety: Integrate critical discussions regarding model bias, interpretability, alignment, and the societal impact of deploying large-scale neural systems.

Industry & Lab Partnership: Facilitate collaboration with AI research labs and tech companies to ensure curriculum relevance and provide students with real-world research opportunities.

Job Requirements
Education: PhD in Computer Science, Artificial Intelligence, or Mathematics is highly preferred.

Experience: Demonstrated experience in deep learning research or applied AI engineering, combined with a passion for academic instruction.

Technical Skills:

Mathematical Depth: Expert knowledge of the optimization landscape, gradient descent variants, and the theoretical underpinnings of loss functions.

Framework Mastery: Advanced proficiency in at least one major DL framework (PyTorch is strongly preferred).

Infrastructure: Understanding of distributed training, GPU memory management, and deployment of large models (e.g., using Triton, ONNX, or quantization techniques).

Soft Skills:

Conceptual Clarity: Ability to distill highly abstract concepts (e.g., backpropagation through time, attention heads, latent spaces) into intuitive visual and conceptual explanations.

Problem-Solving: A "researcher's mindset"—tenacious, methodical, and creative when dealing with non-converging models or opaque data issues.

Intellectual Humility: A commitment to the scientific method and a willingness to stay updated in a field that moves at a breakneck pace.

Inspiration: Ability to ignite excitement in students about the transformative potential of deep learning.

Benefits
Competitive Compensation: Attractive salary package with opportunities for research grants and industry consulting.

Cutting-Edge Resources: Access to dedicated GPU/TPU compute clusters and state-of-the-art AI development environments.

Scholarly Freedom: Support for attending top-tier global AI conferences, publishing open-source research, and sabbatical opportunities.

Comprehensive Benefits: Full insurance coverage, academic leave, and retirement benefits in accordance with local labor laws.

Career Growth: Clearly defined paths toward Tenured Professor, AI Lab Director, or lead research roles in private sector R&D.

Deep Learning Lecturer, Assistant Professor of AI, Research Scientist (Academic), Neural Network Specialist, AI/ML Faculty.

Industry Keywords: Neural Networks, Transformers, Computer Vision, NLP, Optimization, Latent Spaces, Generative AI, PyTorch, GPU Computing.

Attributes: Analytical, Rigorous, Visionary, Articulate, Persistent, Innovative

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