AI Research Career Path & Skills Map
Becoming an AI researcher isn’t about memorizing formulas — it’s about building a layered set of skills that stack over time. These twenty-four milestones describe what real-world progress looks like, from your first line of code to leading original research.
Learn to Read and Reproduce Papers
Start by understanding papers deeply enough to reimplement their results; it’s how researchers learn to think rigorously.
Understand Linear Algebra and Calculus for Models
Grasp matrix operations, gradients, and partial derivatives — the mathematical language of optimization.
Master Python and NumPy
Become fluent in numerical programming, vectorized computation, and debugging; all deep learning builds on this.
Train a Simple Neural Network from Scratch
Write one yourself using only NumPy to internalize how forward and backward passes actually work.
Understand the Full ML Lifecycle
Learn data collection, preprocessing, training, evaluation, and deployment as one connected pipeline.
Use PyTorch Professionally
Be able to build, debug, and visualize models with full control of gradients, losses, and GPU memory.
Read and Understand Research Frameworks (e.g., NeMo, JAX, DeepSpeed)
Know how large models are structured and scaled in real-world codebases.
Fine-Tune a Pretrained Model
Execute your own PEFT or LoRA fine-tune on a public dataset, analyze the results, and document findings.
Experiment Methodically
Track hypotheses, run small controlled tests, and record metrics. Reproducibility is a professional virtue.
Use Version Control and Experiment Tracking
Master Git, DVC, and tools like Weights & Biases to manage data, code, and models with discipline.
Analyze Failures
Learn to diagnose overfitting, exploding gradients, misaligned loss functions, and data bugs calmly and scientifically.
Benchmark and Compare Models
Understand metrics like accuracy, F1, BLEU, ROUGE, and perplexity — and what they really indicate.
Understand Hardware and Scaling
Learn GPU memory limits, CUDA basics, mixed precision, and distributed training. Efficiency is power.
Read the Source Code of Frameworks
Study PyTorch or JAX internals. Reading real source is how you bridge from user to contributor.
Implement a Research Idea
Take a new idea from a paper or your own notebook, code it, and run it on real data. This is where creativity begins.
Write Clear Research Reports
Communicate methods and results clearly; the skill of writing defines whether your work spreads.
Contribute to Open Source
Fix a bug, add a feature, or release your own project; community contribution builds credibility and depth.
Collaborate Across Disciplines
Work with engineers, linguists, and designers; great research thrives on cross-pollination.
Develop Intuition for Model Behavior
Build a “feel” for what models are doing — loss curves, token patterns, attention maps.
Build and Maintain Compute Infrastructure
Understand Docker, Kubernetes, and cluster management to support large experiments independently.
Stay Current with the Literature
Develop a rhythm for scanning arXiv, reading conference papers, and summarizing key advances.
Design Your Own Experiments
Move beyond tutorials; define hypotheses, metrics, and protocols — then test them.
Mentor and Teach Others
Explaining ideas clearly to students or colleagues is proof that you’ve mastered them.
Publish and Reflect
Write papers, blog posts, or documentation that share lessons and failures. The final step of learning is teaching.