ML Engineer (Machine Learning Engineer)
A software engineer specialized in machine learning: designs, trains and integrates models into production systems.
Why this hire matters.
The ML engineer fills the missing link between research and product: engineer enough to write production code, ML enough to understand, adapt and train models. They turn a data scientist's idea into a feature that holds up under load, latency and cost — and, increasingly, design ML systems end-to-end themselves. Without one, your AI ambitions depend on a data scientist who has never deployed, or a backend engineer treating the model as a black box: either way, projects stall. In the 2025-2026 market it's one of the most contested profiles: AI-native companies absorb the supply and pull salaries up. Test the dual foundation in interviews — production code AND real understanding of models.
Key missions.
- Design ML systems end-to-end (data → model → API)
- Train, fine-tune and evaluate models
- Integrate models into the product (latency, cost, reliability)
- Build feature and training pipelines
- Optimize inference (quantization, batching, GPU)
- Collaborate with data scientists and product teams
- Monitor performance and drift in production
Skills.
Technical skills
- Production-grade Python
- PyTorch / ML frameworks
- Serving and APIs (FastAPI, Triton)
- LLM fine-tuning and evaluation
- Cloud and containerization
Expected qualities
- Engineering pragmatism
- Experimental rigor
- Collaboration
Common stack
Salaries 2025-2026
| Level | Experience | Annual gross base |
|---|---|---|
| Junior | 0-2 yrs | 45–60 k€ |
| Mid-level | 2-5 yrs | 60–80 k€ |
| Senior | 5-8 yrs | 72–95 k€ |
| Lead / Staff | 8+ yrs | 95–130 k€ |
Paris market ranges, 2025-2026.
Outside the Paris region, expect 10 to 20 % less.
Sources : Expira 2026 · Silkhom 2026 · Glassdoor France 2026 · Michael Page 2026
Hiring this profile.
Typical background
Software engineer trained in ML, or a data scientist who became a genuine engineer. ML shipped to production is the proof, not certificates.
When to hire
As soon as ML must become a real product feature — often the first AI hire in a product team; seed/Series A for AI-natives.
Career path
Senior → Staff ML Engineer / Lead → Head of ML → Chief Data & AI Officer.