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mlmentorship

Free prep resources

Research Engineer preparation path

Balance implementation and systems depth with scientific skepticism. Confirm whether the title is research-heavy or software-heavy.

Start with a closed-book baseline

Do not read the full path first.

Attempt the first scored question now. Use the result to decide which later steps need repair. Start first practice
  1. 01

    Confirm role mix

    Map research, modeling, engineering, and product expectations.

  2. 02

    Implement a core primitive

    Build the mechanism correctly and explain dimensions and complexity.

  3. 03

    Add systems implementation

    Handle memory and performance without hiding behind a library.

  4. 04

    Model the workload

    Calculate parameters, memory, FLOPs, batch, communication, time, and a measured parallel layout.

  5. 05

    Reason about inference

    Use measurement and cost models rather than a list of optimizations.

  6. 06

    Design discriminating evidence

    Control compute and tuning, use paired resampling, and test alternative explanations.

  7. 07

    Critique research fairly

    Prioritize the threat that most changes the central claim.

  8. 08

    Prepare translation stories

    Show failed experiments, implementation decisions, and research-to-production impact.

  9. 09

    Simulate the loop

    Run ML implementation, systems, research depth, breadth, and project rounds.

After the core path

Add only the format and subject that recruiting confirmed. Do not add every frontier lab or specialist topic.

Choose one domain supplement · Add the upper-IC level overlay · Check current lab formats · Run the role simulation