ML Engineer
Put machine learning models into production with scalable and reliable infrastructure.
ML Engineer
The ML Engineer bridges the gap between data science and production engineering. As companies move from ML experiments to production systems, the demand for engineers who can deploy, scale, and maintain ML models has skyrocketed. With GenAI requiring massive infrastructure, ML Engineers who understand both the science and the engineering are commanding top salaries across Europe and the US.
Skills
LinkedIn Optimization
- 1
Show experience with model deployment at scale
- 2
Highlight projects with real production metrics
- 3
Post about MLOps trends and best practices
- 4
Connect with ML leads at target companies
- 5
Mention specific ML infrastructure you've built
CV & Resume
- 1
Quantify: "Deployed model serving 1M predictions/day"
- 2
Detail ML infrastructure and pipeline experience
- 3
Show end-to-end ML lifecycle management
- 4
Include monitoring and model drift detection experience
- 5
Highlight experience with feature stores and experiment tracking
Outreach Strategy
- 1
Research what ML infrastructure the company uses
- 2
Mention specific ML challenges you can help solve
- 3
Share a case study of ML deployment at scale
- 4
Connect with ML engineers already at the company
- 5
Demonstrate understanding of MLOps best practices
Interview Prep
- 1
Prepare ML system design scenarios
- 2
Know the full ML lifecycle from training to monitoring
- 3
Practice explaining trade-offs in model serving architectures
- 4
Have examples of debugging production ML systems
A day as ML Engineer
Review model performance dashboards, optimize inference latency, build and maintain ML pipelines, collaborate with data scientists on model deployment, work on feature engineering and experiment infrastructure.
Career Path
Common mistakes
Only showing research projects without production experience
Not mentioning infrastructure and deployment skills
Ignoring monitoring and observability in your profile
Not having cloud ML platform experience
Forgetting to mention collaboration with data scientists
Essential tools
FAQ
Average salary for a ML Engineer?
The average salary for a ML Engineer in Europe ranges between 60K - 110K EUR. This varies by experience, location, and company type.
Key skills for a ML Engineer?
Main technical skills: Python, MLOps, Cloud ML, Docker, Data Pipelines. Soft skills like communication and teamwork are equally important.
Career path for a ML Engineer?
Typical path: Junior ML Engineer > Mid ML Engineer > Senior ML Engineer > Staff ML Engineer > Head of ML Engineering > VP of AI Infrastructure.