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Salary guides, role explainers, and day-in-the-life features for AI professionals in Malaysia. Written to be honest, not aspirational.

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ML Engineer

Builds and ships production machine learning systems

Salary (MY)

RM 10k – 22k / mo

Mid to senior. Junior roles start from RM 6k.

Key skills

PythonPyTorch / TensorFlowMLOpsSQLDockerSystem design

A typical day

9:00

Monitoring review

Admin / ops

Check model performance dashboards. Flag any drift in fraud detection recall — looks like a 0.8% drop over 48 hours, worth investigating.

9:30

Code review

Collaboration

Review two PRs from junior engineers. One has a subtle data leakage issue in the feature pipeline. Leave detailed comment explaining why it matters in production.

11:00

Experiment analysis

Deep work

New model architecture finished training overnight. 3% improvement on validation set but 40% slower at inference. Write up trade-off analysis for the team.

13:30

Cross-functional sync

Collaboration

Product wants new feature in three weeks. Honest answer is six. Negotiate scope — trimming three requirements gets it to four weeks without compromising quality.

15:00

Deep engineering work

Deep work

Redesigning feature store to support real-time lookups for the fraud model. This is the work that moves the architecture forward. Block calendar, mute Slack.

17:30

Documentation

Admin / ops

Write runbook for the new serving infrastructure. Unglamorous. Essential. Saves hours of debugging at 2 AM when something inevitably breaks.

The honest realities

More engineering than science — most of the job is pipelines, monitoring, and debugging.
The model is rarely the bottleneck. Data quality and infrastructure usually are.
You will be on-call. Production systems break at inconvenient times.

How people get here

Software engineer or data scientist who moved into productionisation. CS or EE background most common but not required.

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