Machine Learning Engineer
An evidence-led route from mathematics and software foundations to evaluated, deployed machine-learning systems.
Route type
Flexible industry guidance
Content reviewed 2026-07-19. Verify current requirements before acting.
Check these before you start
Eligibility and employer requirements can change. Confirm the applicable route first.
- Target a role clearly: ML engineer, applied scientist, data scientist or MLOps.
- Check current postings because degree, research and production requirements vary widely.
Your path
Stage-by-stage roadmap
Complete each stage with evidence. Progress means demonstrated capability, not just watched lessons.
- 01
Math, code and data foundation
Become comfortable with Python, algorithms, linear algebra, probability and data work.
- Practise Python, SQL and data structures.
- Study linear algebra, calculus, probability and statistics.
Evidence to complete this stage
Reproducible notebooks plus tested data-processing code.
- 02
Learn and evaluate models
Understand classical ML, validation, leakage, bias and meaningful metrics.
- Implement and compare baseline models.
- Use correct train, validation and test design.
- Write model cards with limitations and failure cases.
Evidence to complete this stage
An experiment report with reproducible metrics and limitations.
- 03
Ship an ML system
Connect data, training, inference, monitoring and user value.
- Build one end-to-end application with a real evaluation set.
- Version data and models; monitor quality and drift.
- Document privacy, fairness and operational risks.
Evidence to complete this stage
A deployed project with evaluation, monitoring and documentation.
- 04
Specialise and apply
Match a credible portfolio to a specific ML problem and hiring bar.
- Choose a domain such as vision, language, forecasting or recommendations.
- Reproduce one strong paper or benchmark honestly.
- Practise ML system design and explain trade-offs.
Evidence to complete this stage
A focused portfolio, experiment history and role-matched resume.
Capability
Skills you must be able to prove
Use outputs, assessments and reviewed work—not certificates alone—to demonstrate these skills.
Model evaluation
Reproducible metrics, baselines and error analysis.
ML engineering
Tested pipelines, serving and monitoring.
Responsible practice
Documented data, limitations and risk controls.
Proof of work
What to build or maintain
Keep these outputs ready for applications, interviews, counselling or professional review as applicable.
- Classical ML experiment with honest baselines
- Deployed end-to-end ML application
- Specialisation project or reproducibility study
Verification
Official and primary sources
Open these links before making eligibility, admission, registration or career decisions.