AI Researcher
Advances the frontier of artificial intelligence by designing experiments, publishing findings, and developing novel techniques in academic or industrial research settings.
Overview
An AI researcher designs and runs experiments to push the boundaries of what machine learning systems can do, then communicates those findings through papers, reports, or direct integration into products. The work spans formulating a clear research question, implementing and evaluating methods rigorously, and situating results honestly within the broader literature. Whether working in academia, at a dedicated research lab, or on an industry team, the role demands intellectual curiosity, mathematical depth, and a high tolerance for results that do not go as expected.
Beginner roadmap
Phase 1: Mathematical and Programming FoundationsWeeks 1-8
Strengthen linear algebra, calculus, probability, and statistics, and build proficiency in Python and a major deep learning framework so that implementing ideas from papers feels natural.
Phase 2: Core Machine Learning and Paper ReadingWeeks 9-16
Study foundational ML methods deeply, practice reproducing results from seminal papers, and develop a habit of critically evaluating claims and experimental design.
Phase 3: Focused SpecializationWeeks 17-24
Choose a specific research area, read widely within it, and run your own small experiments that extend or question existing findings.
Phase 4: Original ContributionWeeks 25-36
Develop an original idea, run a complete experimental evaluation, write it up clearly, and share it publicly through a preprint, blog post, or workshop submission.
Portfolio ideas
- A well-documented reproduction of a recent influential paper with notes on implementation choices and deviations.
- An original experiment that extends a published method and honestly reports both improvements and failures.
- A clear technical blog post explaining a complex research concept to a broader audience.
- An open-source repository with clean code, thorough documentation, and reproducible results.
- A literature review that synthesizes findings across several papers and identifies open questions.
Salary & sources
Salary ranges vary widely by region, seniority, industry, and company. Check current data on reputable salary aggregators (placeholder - verify before publishing).
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Frequently asked questions
A PhD is common for roles at top research labs and in academia, but it is not a strict requirement for every research position. Strong publications, open-source contributions, and demonstrated research ability can substitute at some organizations, particularly in industry research teams.
Researchers focus on discovering new techniques, running controlled experiments, and publishing results that advance the field. Machine learning engineers take those techniques and build them into reliable production systems. The boundaries blur in practice, especially at companies that publish research and ship products.
Reproduce recent papers and share your code, contribute to open-source research repositories, engage in communities like Papers With Code, and write up your experiments in blog posts or technical reports. Public track records matter as much as credentials.
Large language models, multimodal systems, alignment and safety, efficient training and inference, agents, and robotics are all highly active. Picking a specific problem you find genuinely interesting and going deep tends to produce better work than chasing trends.
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