Explore
Applied AI systems
Machine Learning Engineer
Mostly data plumbing, occasionally the model.
Typical pay
$120k-$220k
How you get in
Software engineering foundation plus applied ML skills
Outlook
Strong, though the entry level is crowded with bootcamp graduates
ML engineers build and deploy machine learning systems in production. The distinguishing skill is software engineering, not model theory — most of the work is pipelines, evaluation and serving infrastructure.
A day in the life
- 9:00aCheck overnight training runs and evaluation metrics
- 10:00aDebug a data pipeline that silently dropped records
- 12:30pLunch
- 1:30pFeature engineering and a retraining experiment
- 3:30pCode review and deployment planning
- 5:00pMonitor a model in production for drift
How to get in
Software engineer into ML
Duration
1-3 years
Cost
$0-$5,000
Credential
None — shipped systems
- Work as a software engineer first; this foundation is not skippable
- Learn the ML stack — PyTorch, data pipelines, evaluation, deployment
- Take on ML infrastructure work at your current company
- Move into an ML engineer role internally, then externally
Graduate degree
Duration
2-6 years after a bachelor's
Cost
$0-$100,000 — PhDs are usually funded
Credential
MS or PhD in computer science, ML or statistics
- Complete a strong quantitative bachelor's
- Enter a master's or funded PhD with an ML focus
- Publish or build substantial applied projects
- Research-heavy roles at large labs generally expect the PhD
Data scientist or analyst into ML
Duration
2-4 years
Cost
$0-$5,000
Credential
None — production systems in your portfolio
- Start as a data analyst or scientist, which is easier to enter
- Push hard on software engineering skills — this is the usual gap
- Take models from notebook to production, which is the actual differentiator
- Move to ML engineering with both the modeling and the shipping demonstrated
What people love
- · Among the highest-paid engineering roles
- · Genuinely difficult and interesting problems
- · Remote-friendly
- · Demand is currently outstripping supply
What wears people down
- · Not an entry-level role — requires strong engineering first
- · Most of the job is data work, not modeling
- · Hype cycles create unrealistic expectations from leadership
- · Tooling changes faster than almost any other field