Machine Learning Engineer (MLE) Roadmap
MLE is “software engineering for learning systems”. The job is not just training models; it’s building systems that improve safely over time.
What gets evaluated
- core ML concepts and evaluation
- data quality, leakage, bias
- production readiness: deployment, monitoring, iteration
- ability to turn product goals into measurable objectives
MLE foundations
ML fundamentals (interview-grade)
- supervised learning: classification/regression
- metrics: precision/recall, ROC-AUC, calibration
- bias/variance and regularization intuition
- offline vs online evaluation
Data + features
- leakage patterns
- label quality and feedback loops
- feature freshness and ownership
Production ML engineering
- training/serving skew
- monitoring for drift and silent failures
- rollback, shadow deploy, A/B testing
12-week plan
Weeks 1–3: Refresh ML fundamentals
- create a one-page “metrics cheat sheet” per problem type
- practice explaining tradeoffs: precision vs recall, latency vs accuracy
Weeks 4–6: ML system design
- 1 prompt/week: ranking/recommendation, fraud/spam, search, forecasting
- use 05-ML-MLOPS/03-ML-System-Design-Templates.md
Weeks 7–9: Production readiness
- monitoring plan, testing plan, rollback plan
- map to ML Test Score rubric
Weeks 10–12: Mock loops
- coding (varies), ML system design, behavioral
Common failure cases
- treating ML like a notebook project
- no plan for data/label issues in production
- confusing metric optimization with product success
- ignoring cost/latency constraints
Company archetype adjustments
- FAANG/high-growth: emphasis on large-scale systems + experimentation.
- Enterprise: emphasis on reliability, governance, model risk management.
- Automotive/industrial: emphasis on safety, verification, and risk control (often stricter acceptance criteria).