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.
Compétences
Optimisation LinkedIn
- 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 et Curriculum
- 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
Stratégie d'Outreach
- 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
Préparation d'Entretiens
- 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
Une journée en tant que 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.
Parcours de Carrière
Erreurs courantes
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
Outils essentiels
Questions fréquentes
Salaire moyen d'un/e ML Engineer?
Le salaire moyen d'un/e ML Engineer en Europe se situe entre 60K - 110K EUR. Cela varie selon l'expérience, la localisation et le type d'entreprise.
Compétences clés pour un/e ML Engineer?
Principales compétences techniques : Python, MLOps, Cloud ML, Docker, Data Pipelines. Les soft skills comme la communication et le travail d'équipe sont tout aussi importants.
Parcours de carrière d'un/e ML Engineer?
Parcours typique : Junior ML Engineer > Mid ML Engineer > Senior ML Engineer > Staff ML Engineer > Head of ML Engineering > VP of AI Infrastructure.
ML Engineer
Soumettez votre profil pour examen. J'étudie personnellement chaque candidature. Si vous êtes qualifié, je vous contacterai avec une analyse détaillée et votre plan d'optimisation personnalisé.