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Roadmap

AI Researcher

Becoming a AI Researcher means building the skills below in roughly this order. The current median pay for AI Researcher roles on Kairos is $286,875 (32 disclosed listings). 17 of these skills appear in live AI Researcher listings right now.

Demand and pay updated 30 min ago.

01

Math & programming foundations

0-6 months
  1. Core

    Research demands more math than applied ML - comfort with proofs, gradients, and probabilistic reasoning.

  2. In demand · 27%
    Core

    Fluent Python with NumPy for fast, correct experiment code.

  3. In demand · 24%
    Core

    The why behind the methods - bias/variance, generalization, and optimization.

    Stanford CS229 ↗
02

Deep learning mastery

6-12 months
  1. In demand · 6.1%
    Core

    Architectures, training dynamics, and the transformer in depth - from the math to the code.

  2. In demand · 5.2%
    Core

    Research-grade frameworks; JAX and CUDA matter for performance-critical work.

  3. Recommended

    Re-implementing results from scratch is the fastest way to research competence.

03

Choose a specialization

12+ months
  1. In demand · 16%
    Optional

    Pretraining, alignment, and evaluation of large language models.

    Stanford CS224N ↗
  2. In demand · 2.4%
    Optional

    Policy learning and RLHF - central to modern alignment and agents.

  3. In demand · 2.7%
    Optional

    Diffusion models, multimodal architectures, and perception.

04

Publish & scale

Ongoing
  1. Recommended

    Track arXiv, write up results, and submit to venues like NeurIPS, ICML, and ACL.

  2. In demand · 11%
    Optional

    Train at scale across many GPUs - the practical bottleneck for frontier work.

Grounded in

Questions

How do I become a AI Researcher?
Work through the roadmap in order: start with the Core foundations, then layer on the Recommended and Optional skills. The path toward research scientist and research engineer roles at AI labs: strong math and programming foundations, deep learning mastery, a specialization (LLMs, RL, vision, or systems), and the ability to read, reproduce, and publish papers.
Is this roadmap based on real sources?
Yes. It is grounded in Stanford CS229 - Machine Learning, Stanford CS224N - NLP with Deep Learning, DeepLearning.AI - Deep Learning Specialization, fast.ai - Practical Deep Learning. Skill demand and pay are measured live from AI job listings on Kairos.