Learn Computer Stuff
Home / AI / Machine Learning / AI Research Engineer
AI / Machine Learning

AI Research Engineer

You build and run the experiments researchers design — training the models, not writing the papers.

Degree usually expected Hard, and the bar is implementation depth rather than novelty.


Can I actually do this?

Not an entry role. Of the three research-facing paths here it has the lowest barrier, because it is judged on engineering rather than publications — that is our reasoning from what the role assesses, not a hiring source we have checked. A PhD is common but not universal; demonstrated ability to reimplement papers is the substitute.

Who it suits. Strong engineers who want to work near research without needing to originate it — you make other people's ideas run, correctly and fast.

Runway. Long. Realistically you need to reach the point where you can reimplement published papers unaided, which is a depth statement rather than a duration we can source.

Coming from another job?

Coming from ML or systems engineering with strong PyTorch? That is the realistic route — this is engineering applied to research, not a change of discipline. Machine Learning Engineer GPU / CUDA Engineer

Also advertised as

  • Research Engineer
  • ML Research Engineer
  • Member of Technical Staff (research)

The route

Four stations, in order. Each one is a thing you finish before the next matters.

  1. Station one

    Learn it free

    Only the best few, deliberately. Every one of these is free to use — the pill on each card says exactly what is and isn't free.

    Practical Deep Learning for Coders (fast.ai)

    Free to learn · no certificate

    Free, complete deep-learning course taught top-down. No certificate, and fast.ai has never pretended otherwise.

    PyTorch tutorials (official)

    Free to learn · no certificate

    The framework virtually all AI research is written in. Free and open source; docs shown for version 2.13.0.

    Attention Is All You Need (the transformer paper)

    Free to learn · no certificate

    The paper the current era is built on, free on arXiv. Reading it is not the exercise — reimplementing it is.

    See the full catalog in the explorer →

  2. Station two

    Attest strategically

    There is no certificate for this role and buying one would be a waste. The knowledge graph lists none, and our vendor research found none. Research engineering is assessed on code: a working reimplementation of a known paper is the currency.

    Nothing here is worth paying for

    No credential needed

    There is no certificate for this role and buying one would be a waste. The knowledge graph lists none, and our vendor research found none. Research engineering is assessed on code: a working reimplementation of a known paper is the currency.

  3. Station three

    Prove it

    A certificate says you passed a test. These say you can do the job.

    A paper reimplemented from scratch

    Pick a published architecture and reproduce it, matching the reported numbers as closely as you can. Document where you diverged and why — the divergences are the interesting part.

    A training run at real scale

    Train something across multiple GPUs and document throughput, cost, and what you changed to improve them.

    An ablation someone can check

    Remove one component of a model and measure what it cost you. Ablations show scientific discipline, not just engineering.

  4. Station four

    Get hired

    Search these exact titles

    • research engineer
    • AI research engineer
    • ML research engineer
    • member of technical staff

    Who hires for this. AI labs, university research groups with engineering budgets, and industrial research divisions.

    A public reimplementation that matches published numbers is checkable by anyone in the field, which makes it unusually strong evidence. That is our reasoning about what is inspectable, not a hiring statistic we have verified.

    On salary

    We don't publish salary estimates. Numbers copied between blogs drift from reality, and a wrong number costs you real negotiating power. When we have a verified public source, it goes here with its date.


Where this route continues

Continues into research scientist roles, usually alongside publications.

· How we verify