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Salary is split by company size rather than one flat range, which makes comparison easier. Local ranges are welcome.
POSITION 06 · Data & AI · Algorithm Engineer
Algorithm Engineer
Train a pattern, then keep it stable in production
Algorithm engineers solve prediction, recommendation and generation problems at scale. Sample design and evaluation usually matter more than model architecture.
Core responsibilities 01 / DUTY
- Translate business goals into optimisable targets
- Build samples and features without leakage
- Monitor drift after deployment
Required skills 02 / SKILL
ML & deep learningLinear algebra & probabilityPython / PyTorchFeature engineeringEvaluation design
Who this suits 06 / FIT
- Strong math foundation
- Cares about production, not only SOTA
FAQ 07 / FAQ
Is a master's degree required?
Research roles often require it, but applied recommendation and risk roles hire on project ownership.
Related industries & roles 08 / LINK
This role commonly appears in these industries and works with these roles:
Before you start · insider notes
Data leakage is the quietest bug
If a feature isn't available at prediction time, offline scores are fiction.
Baselines beat novelty
Ship a simple rule first so you can measure whether a complex model is worth its cost.
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Company size × salary
Illustrative ranges · not official statistics · community-editableThe same role pays differently — and asks for different things — at different company sizes. Editorial reference ranges; corrections welcome.
| Company size | Monthly salary | What matters |
|---|---|---|
| Startup / <50 | 20–35K | Validate use cases & baselines |
| Growth / 50–500 | 28–48K | Samples, features, deployment loop |
| Large / 500–5k | 35–60K | Evaluation & inference cost |
| Platform / >5k | 45–80K | Platform capabilities & research |
Abilities required
Five dimensions, level 1–5
Math
5/5
Engineering
4/5
Experimentation
5/5
Business sense
4/5
Evaluation rigor
5/5
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Algorithm Engineer · Offline Up, Online Flat
What surrounds the model often decides whether it works.
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Discussion
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The interview questions are useful; a note on explaining your reasoning during live coding would help.
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