Research Engineering

Selected references on research taste, engineering judgment, and doing useful technical work.

Research Taste

• You and Your Research
  Hamming’s classic essay on choosing important problems and organizing a life around serious work.

• An Opinionated Guide to ML Research
  Practical advice on developing taste and becoming effective in machine learning research.

• Principles of Effective Research
  A useful frame for research as a skill that can be deliberately improved.

• Fast
  Examples of ambitious work happening faster than conventional expectations.

Engineering Judgment

• Design Docs for Machine Learning Systems
  Good reminder that clear technical writing is part of engineering, not a decorative artifact after the work.

• Machine Learning Design Patterns
  Useful examples of recurring patterns in applied ML systems.

• Reproducing Deep Reinforcement Learning
  A useful account of how fragile experimental claims can be when implementation details, seeds, and environments are underspecified.

• ML Productivity
  Practical notes on making machine learning experimentation less ad hoc.

• Harvard CS197: Communicating Computer Science Research
  Useful for turning technical work into a clear research artifact.

Skepticism

• Your LLM-assisted scientific breakthrough probably isn’t real
  A helpful caution against mistaking plausible machine-generated research narratives for evidence.

Why This Page Exists

Research engineering is the bridge between asking a good question and producing evidence that survives contact with reality.

I care about this because most of the problems I am interested in are not solved by ideas alone. They require careful experiments, clean systems, good measurement, and the discipline to notice when the result is only plausible rather than faithful.