Machine Learning Engineer Jobs in Madrid 2026: Application Guide

You keep seeing “Machine Learning Engineer, Madrid” roles on LinkedIn, but every job post seems to want five tools, three cloud platforms, production experience, Spanish, English, and somehow “startup energy” on a Tuesday morning. If you are trying to land an ML engineer job in Madrid in 2026, you need more than a nice Python CV. You need to know who is hiring, what they pay, what skills matter, and how to apply without getting filtered out before a human sees your name.

Why Madrid Is a Strong ML Job Market in 2026#

Madrid has become one of the most active AI hiring cities in Europe. It is not just banks and consulting firms anymore. You now have fintech, travel tech, cybersecurity, healthtech, energy, retail, and global tech companies building data and AI teams in the city.

The big reason is simple: Madrid is cheaper than London, Amsterdam, Berlin, or Paris, but still has strong talent, good infrastructure, and access to the EU market.

Companies hiring ML engineers in Madrid often fall into these groups:

1. Spanish enterprise companies

   • BBVA
   • Santander
   • Telefónica
   • Repsol
   • Iberdrola
   • Inditex tech teams

2. International tech companies with Madrid offices

   • Amazon
   • Microsoft
   • Google Cloud partners
   • Oracle
   • IBM
   • Accenture
   • NTT DATA

3. Scaleups and product companies

   • Cabify
   • Fever
   • Jobandtalent
   • Typeform
   • Factorial
   • Spotahome

4. Consulting and AI service firms

   • Minsait
   • Capgemini
   • Deloitte
   • KPMG
   • EY
   • Cognizant

5. Remote-first EU companies hiring from Spain

   • Revolut
   • Wise
   • Booking.com
   • Klarna
   • Spotify
   • GitLab

Madrid is especially strong if your ML experience connects to business problems: fraud detection, pricing, churn prediction, recommendation systems, logistics, NLP, computer vision, demand forecasting, or generative AI tools for internal teams.

What Machine Learning Engineer Jobs Actually Look Like In Madrid#

A lot of job seekers get stuck because they apply to every role with “machine learning” in the title. But in Madrid, ML engineer can mean several different jobs.

Before you apply, match yourself to the right version.

1. Product Machine Learning Engineer

This is the classic ML engineer job. You build models that improve a product.

You might work on:

• Recommendations for an e-commerce app
• Search ranking for a marketplace
• Fraud detection in fintech
• Demand prediction for mobility
• Personalization in a travel app

Common tools:

• Python
• scikit-learn
• PyTorch or TensorFlow
• SQL
• Docker
• Kubernetes
• AWS, GCP, or Azure
• MLflow, Airflow, or Kubeflow

Typical Madrid salary in 2026:

• Junior: €35k to €45k
• Mid-level: €45k to €65k
• Senior: €65k to €90k
• Staff or lead: €90k to €120k+

You will usually need strong coding skills, not just notebooks. If your CV says “trained models” but not “deployed models,” you may struggle.

2. Data Scientist With ML Engineering Skills

Many Madrid companies still use “Data Scientist” when they really want someone who can build predictive models and ship them into production.

You might work on:

• Customer segmentation
• Marketing attribution
• Forecasting
• Risk models
• A/B testing
• Business dashboards with ML outputs

Common tools:

• Python
• SQL
• Pandas
• scikit-learn
• XGBoost
• Tableau, Power BI, or Looker
• Airflow
• Git

Typical Madrid salary in 2026:

• Junior: €32k to €42k
• Mid-level: €42k to €60k
• Senior: €60k to €80k
• Lead: €80k to €100k

This route is good if you are moving from analytics into ML. But you need to show business impact, not only model accuracy.

3. MLOps Engineer

MLOps is growing fast in Madrid because companies are tired of models stuck in Jupyter notebooks.

You might work on:

• Model deployment pipelines
• Feature stores
• Model monitoring
• CI/CD for ML
• Infrastructure for model training
• Cloud cost control
• Experiment tracking

Common tools:

• Docker
• Kubernetes
• Terraform
• GitHub Actions or GitLab CI
• AWS SageMaker, Azure ML, or Vertex AI
• MLflow
• Prometheus and Grafana
• Python
• Bash

Typical Madrid salary in 2026:

• Mid-level: €50k to €70k
• Senior: €70k to €95k
• Lead: €95k to €125k

If you have DevOps or backend engineering experience, MLOps can be your best way into AI roles.

4. Generative AI Engineer

Yes, the hype is still here. But by 2026, companies are less interested in “I built a chatbot” and more interested in safe, reliable AI systems that save money or improve workflows.

You might work on:

• RAG systems
• Internal knowledge assistants
• Document processing
• Customer support automation
• LLM evaluation
• Prompt testing frameworks
• AI governance tools

Common tools:

• Python
• LangChain or LlamaIndex
• OpenAI API
• Azure OpenAI
• Hugging Face
• Vector databases like Pinecone, Weaviate, or pgvector
• FastAPI
• Docker
• SQL

Typical Madrid salary in 2026:

• Mid-level: €50k to €70k
• Senior: €70k to €100k
• Lead: €100k to €130k

For these roles, your portfolio matters a lot. A clean GitHub project with RAG, evaluation, logging, and deployment beats ten vague “AI enthusiast” lines.

Madrid ML Engineer Salaries Compared To Other Cities#

Madrid usually pays less than London, Zurich, Amsterdam, or San Francisco. But the cost of living can make the offer more attractive than it looks.

Here is a rough 2026 comparison for mid-level to senior ML engineer roles:

For remote US companies hiring in Spain, salaries can go higher. A senior ML engineer working remotely from Madrid for a US company might see €90k to €140k, sometimes more if equity is included.

But be careful with contractor roles. A $120k contractor offer sounds great until you account for taxes, social security, no paid holidays, accounting, and job security.

Companies Hiring Machine Learning Engineers In Madrid#

You do not need to apply everywhere. Better to build a targeted list and adapt your CV properly.

Here are companies worth watching in 2026.

BBVA

BBVA has strong AI, risk, fraud, credit scoring, and personalization use cases. They hire data scientists, ML engineers, MLOps engineers, and AI product roles.

Good fit if you have:

• Finance or risk experience
• Strong SQL and Python
• Model governance knowledge
• Experience with explainability
• Spanish and English communication skills

Salary range: about €45k to €95k depending on level.

Santander

Santander has large tech teams and AI roles across risk, compliance, customer analytics, fraud, and automation.

Good fit if you have:

• Enterprise ML experience
• Cloud experience
• Data engineering basics
• Risk modeling or banking domain knowledge

Salary range: about €45k to €100k.

Telefónica

Telefónica works on AI for networks, customer support, cybersecurity, churn, and product personalization.

Good fit if you have:

• Time series experience
• NLP or LLM experience
• Big data tools
• Cloud and production deployment skills

Salary range: about €42k to €90k.

Cabify

Cabify is one of Madrid’s most interesting product companies for ML. Mobility gives you pricing, routing, fraud, demand forecasting, marketplace balance, and user behavior problems.

Good fit if you have:

• Recommender systems
• Forecasting
• Optimization
• Experimentation
• Product mindset

Salary range: about €50k to €100k.

Fever

Fever works in entertainment, events, pricing, recommendations, and demand prediction. ML can connect directly to revenue, which is good for your career.

Good fit if you have:

• Ranking and recommendation experience
• Experiment design
• Product analytics
• Fast startup execution

Salary range: about €45k to €90k.

Amazon Madrid

Amazon hires for applied science, ML engineering, data engineering, and software roles in Spain. The bar is high, and interviews can be intense.

Good fit if you have:

• Strong algorithms
• Production ML
• System design
• Clear business impact stories
• Good interview preparation

Salary range: often €70k to €140k total compensation for experienced roles, depending on level and stock.

Microsoft And Cloud Partners

Microsoft and its partner network hire AI engineers, cloud ML engineers, and solution architects in Madrid.

Good fit if you have:

• Azure ML
• Azure OpenAI
• Enterprise AI
• Client-facing skills
• Security and compliance awareness

Salary range: about €55k to €120k depending on role and seniority.

Skills Madrid Employers Want In 2026#

You do not need every tool. But you need a clear skill stack that says, “I can build this thing and ship it.”

Core Technical Skills

Most Madrid ML engineer job posts ask for some mix of:

1. Python

   • Clean code
   • Type hints
   • Testing
   • Packaging
   • API development

2. Machine learning fundamentals

   • Supervised learning
   • Unsupervised learning
   • Model validation
   • Feature engineering
   • Bias and variance
   • Metrics selection

3. Deep learning

   • PyTorch or TensorFlow
   • Transfer learning
   • Embeddings
   • GPU basics
   • Model fine-tuning

4. SQL

   • Joins
   • Window functions
   • Aggregations
   • Performance basics
   • Data quality checks

5. Cloud

   • AWS, Azure, or GCP
   • Storage
   • Compute
   • Permissions
   • Deployment basics

6. Deployment

   • Docker
   • FastAPI
   • CI/CD
   • Monitoring
   • Logging

7. MLOps

   • MLflow
   • Feature stores
   • Model registries
   • Drift monitoring
   • Automated retraining

GenAI Skills That Actually Help

If you want generative AI roles, do not just list ChatGPT.

Show you can build useful systems.

Useful skills include:

• RAG architecture
• Chunking strategies
• Embedding models
• Vector search
• LLM evaluation
• Prompt versioning
• Guardrails
• Cost tracking
• Latency improvement
• Privacy-aware design

Good portfolio project idea:

1. Build a Spanish-English document assistant.
2. Use public legal, HR, or finance documents.
3. Add citation-based answers.
4. Track hallucinations.
5. Deploy with FastAPI.
6. Add a small evaluation set.
7. Write a README with tradeoffs.

That one project can support multiple Madrid applications, especially for banks, insurance, consulting, and enterprise AI roles.

Spanish Language Requirements: Do You Need Spanish?#

Short answer: not always, but it helps a lot.

Madrid has many English-speaking tech teams, especially in international companies and startups. But Spanish becomes more important in:

• Banking
• Public sector projects
• Consulting
• Telecom
• Healthcare
• Client-facing AI roles
• Leadership roles

A realistic language guide:

If your Spanish is B1 or B2, mention it honestly. Do not write “fluent” if you panic when HR calls.

A good CV line:

• Languages: English C1, Spanish B2, French B1

If you are still learning, write:

• Languages: English C1, Spanish A2, actively improving through weekly lessons

That is better than hiding it.

Work Authorization And Visa Notes#

If you already have EU citizenship or the right to work in Spain, make it clear near the top of your CV.

For example:

• Work authorization: EU citizen, eligible to work in Spain
• Work authorization: Spanish work permit valid through 2028
• Location: Madrid, open to hybrid roles

If you need sponsorship, you can still apply, but be strategic. Larger companies are more likely to sponsor than small startups.

More sponsor-friendly options include:

• Amazon
• Microsoft
• Google partner firms
• Accenture
• IBM
• Oracle
• Santander
• BBVA
• Large consultancies

Do not make recruiters guess. Put your status clearly but calmly.

How To Build A Madrid-Ready ML Engineer CV#

Your CV should be boring in the best way. Easy to scan, full of proof, and matched to the role.

Recruiters are not reading your CV with a coffee and soft music. They are scanning it in 12 seconds between calls.

Best CV Structure

Use this order:

1. Name, location, contact, LinkedIn, GitHub
2. Short headline
3. Technical skills
4. Professional experience
5. Projects
6. Education
7. Certifications
8. Languages and work authorization

Strong Headline Examples

Bad:

• Passionate AI professional looking to change the world

Better:

• Machine Learning Engineer, Python, PyTorch, AWS, MLflow, Madrid

Even better:

• Machine Learning Engineer with 4 years in fraud detection, Python, PyTorch, AWS, and production model deployment

Skills Section Example

Keep it tight.

Technical Skills

• Languages: Python, SQL, Bash
• ML: scikit-learn, XGBoost, PyTorch, TensorFlow, Hugging Face
• MLOps: MLflow, Docker, Kubernetes, GitHub Actions, Airflow
• Cloud: AWS SageMaker, S3, Lambda, ECS
• Data: PostgreSQL, Spark, BigQuery
• GenAI: RAG, embeddings, vector search, LangChain, Azure OpenAI

Do not list 60 tools. It makes you look scattered.

Experience Bullet Formula

Use this:

Built X using Y, resulting in Z.

Examples:

• Built a fraud detection model using XGBoost and transaction features, reducing false positives by 18% across 2.4 million monthly payments.
• Deployed a churn prediction API with FastAPI, Docker, and AWS ECS, cutting weekly manual scoring time from 6 hours to 20 minutes.
• Created MLflow experiment tracking for 12 active models, improving reproducibility and reducing model rollback time by 40%.
• Developed a RAG assistant using Azure OpenAI and pgvector, answering internal policy questions with cited sources and reducing HR ticket volume by 22%.

Numbers matter. If you cannot share exact company numbers, use safe ranges or relative improvements.

Examples:

• Improved model precision by 11% on a validation set of 500k records.
• Reduced inference latency from 900ms to 280ms.
• Supported forecasting for more than 80 retail locations.
• Processed over 10GB of daily event data.

How To Tailor Your Application For Madrid Jobs#

Do not send one generic CV to 100 roles. That is how you end up thinking the market is broken.

Use a simple targeting system.

Step 1: Pick Your Target Lane

Choose one or two:

1. ML Engineer
2. MLOps Engineer
3. Data Scientist
4. GenAI Engineer
5. Applied Scientist
6. AI Consultant

Your CV should reflect that lane.

If the job title is MLOps Engineer and your CV opens with “Data Analyst,” you are making the recruiter work too hard.

Step 2: Mirror The Job Description

If the job post says:

• Python
• AWS
• MLflow
• Docker
• Model monitoring
• Fraud detection

Your CV should include those exact words if you actually have them.

Not synonyms. Not cute wording. Exact words.

Applicant tracking systems and recruiters both like clear matches.

Step 3: Change Your Top 6 Bullets

For each application, update the most visible bullets.

For a fintech ML role, move fraud, risk, anomaly detection, and explainability higher.

For a GenAI role, move RAG, embeddings, LLM evaluation, and deployment higher.

For MLOps, move CI/CD, Docker, Kubernetes, MLflow, monitoring, and cloud higher.

Step 4: Use A Simple Cover Message

Most people write cover letters that sound like legal disclaimers.

Keep it short.

Example:

Hi, I’m applying for the Machine Learning Engineer role in Madrid. I have 4 years of experience building and deploying Python ML models, including fraud detection and churn prediction systems on AWS. In my last role, I deployed an XGBoost model through FastAPI and Docker that reduced false positives by 18%. I’m based in Madrid and available for hybrid work. Thanks for considering my application.

That is enough.

Interview Process For ML Engineer Jobs In Madrid#

Madrid interview loops vary, but most serious ML roles follow a pattern.

Common Interview Stages

1. Recruiter screen

   • Salary expectations
   • Work authorization
   • English and Spanish level
   • Notice period
   • Hybrid availability

2. Technical screen

   • Python
   • SQL
   • ML fundamentals
   • Past projects
   • Cloud basics

3. Take-home task or live coding

   • Model training task
   • Data cleaning
   • API deployment
   • SQL challenge
   • ML system design

4. Hiring manager interview

   • Business impact
   • Team fit
   • Stakeholder communication
   • Ownership

5. Final interview

   • Culture fit
   • Salary negotiation
   • Role expectations

Questions You Should Prepare

Expect questions like:

• How do you choose the right metric for an imbalanced classification problem?
• How do you detect data drift in production?
• How would you deploy a model as an API?
• What is the difference between batch and real-time inference?
• How do you avoid leakage in model training?
• How would you design a recommendation system for events in Madrid?
• How do you evaluate a RAG system?
• What would you monitor after deploying a fraud model?
• How do you explain a model to a non-technical stakeholder?
• Tell me about a model that failed.

That last one is important. Good ML engineers have scars.

Your Best Interview Stories

Prepare 5 stories:

1. A model you shipped to production
2. A model that improved a business metric
3. A messy data problem you fixed
4. A disagreement with a stakeholder
5. A failure and what you changed after it

Use numbers. Use simple language. Do not spend six minutes explaining gradient boosting unless they ask.

Salary Negotiation In Madrid#

Many candidates underprice themselves because they are nervous. Do not blurt out the lowest number that would keep your landlord calm.

Before interviews, decide:

1. Your minimum acceptable salary
2. Your target salary
3. Your “yes immediately” salary
4. Your flexibility on hybrid, bonus, equity, and learning budget

What To Say When Asked Salary Expectations

For mid-level ML roles:

Based on the role and Madrid market, I’m targeting €55k to €65k, depending on total package and responsibilities.

For senior roles:

For senior ML engineer roles with production ownership, I’m usually looking at €75k to €90k, depending on equity, bonus, and flexibility.

For GenAI or MLOps lead roles:

For this type of lead AI role, I’d expect something in the €90k to €115k range, depending on scope and total compensation.

Do not give a number before you understand the role. A role managing three production models across Europe is not the same as a notebook-heavy analyst role.

Benefits To Check

Ask about:

• Bonus
• Equity or RSUs
• Meal vouchers
• Health insurance
• Pension contributions
• Training budget
• Conference budget
• Remote work policy
• Home office allowance
• On-call expectations
• Cloud certification support
• Relocation support

A €65k offer with remote flexibility, training budget, and no on-call may beat a €72k offer with chaos energy.

Best Job Boards For ML Engineer Jobs In Madrid#

Use more than LinkedIn. Everyone is on LinkedIn, which means every role gets flooded.

Try:

1. LinkedIn Jobs

   • Best for big companies and recruiter outreach

2. InfoJobs

   • Very common in Spain

3. Wellfound

   • Better for startups

4. Otta

   • Good for tech product roles

5. Remote OK

   • Useful for remote AI roles

6. Hacker News Who Is Hiring

   • Great for startup and remote roles

7. Company career pages

   • Best for BBVA, Santander, Amazon, Cabify, Fever, Microsoft

8. Meetup and local AI communities

   • Great for hidden roles

Madrid has active data and AI communities. Look for meetups around Python Madrid, PyData Madrid, Google Developer Groups, cloud communities, and AI product events.

Portfolio Projects That Help You Stand Out#

If you do not have strong production ML experience yet, your portfolio has to carry more weight.

Do not upload ten half-finished notebooks. Build two or three polished projects.

Project 1: Madrid Rental Price Predictor

Useful if you want marketplace, real estate, or pricing roles.

Include:

• Data cleaning
• Feature engineering
• XGBoost or LightGBM
• Model evaluation
• FastAPI endpoint
• Docker setup
• README with limitations

Project 2: Spanish Customer Support RAG Bot

Useful for GenAI roles.

Include:

• Spanish documents
• Embeddings
• Vector database
• Source citations
• Evaluation set
• Hallucination checks
• Simple web interface

Project 3: Fraud Detection Pipeline

Useful for fintech and banking.

Include:

• Imbalanced classification
• Precision, recall, ROC-AUC, PR-AUC
• Feature importance
• Model monitoring idea
• Batch scoring pipeline
• MLflow tracking

Project 4: Demand Forecasting For Events Or Mobility

Useful for Cabify, Fever, travel, retail, and logistics.

Include:

• Time series features
• Baseline model
• Gradient boosting or deep learning model
• Error analysis
• Business recommendations

Every project needs a README that answers:

1. What problem does this solve?
2. What data did you use?
3. What model did you choose?
4. How did you evaluate it?
5. How would you deploy it?
6. What would you improve next?

That is how you look like an engineer, not just someone following a tutorial.

A 30-Day Application Plan#

If you want results, treat job search like a sprint.

Week 1: Positioning

Do this:

• Choose target role type
• Rewrite your CV headline
• Update your skills section
• Add 4 to 6 metric-based bullets
• Clean your LinkedIn
• Pin your best GitHub projects
• Build a list of 40 target companies

Week 2: Applications

Apply to:

• 10 highly matched roles
• 10 medium-match roles
• 5 reach roles

For each role:

• Tailor the top third of your CV
• Match keywords honestly
• Send a short cover message
• Track everything in a spreadsheet

Week 3: Networking

Message:

• 10 ML engineers in Madrid
• 5 recruiters
• 5 hiring managers
• 5 alumni or former colleagues

Simple message:

Hi Ana, I saw you work in ML at Cabify. I’m applying for ML engineer roles in Madrid and noticed your team works on forecasting and marketplace problems. Would you be open to a quick 10-minute chat? Happy to work around your schedule.

Do not ask for a job immediately. Ask for advice first.

Week 4: Interview Prep

Practice:

• 20 Python questions
• 20 SQL questions
• 10 ML theory questions
• 5 system design prompts
• 5 project stories
• 3 salary conversations

Record yourself once. Yes, it feels weird. Do it anyway.

Common Mistakes To Avoid#

You can be good and still lose because your application looks weak.

Avoid these:

1. Writing a CV that is too academic

   • Companies want impact, shipping, and ownership.

2. Listing tools without proof

   • “AWS” means little unless you say what you built with it.

3. Ignoring SQL

   • Many ML roles in Madrid test SQL early.

4. Applying only to big names

   • BBVA and Amazon are great, but competition is heavy.

5. Being vague about work authorization

   • Recruiters hate uncertainty.

6. Overdoing GenAI buzzwords

   • Show systems, evaluation, and business value.

7. Not preparing salary numbers

   • You should know your range before the call.

8. Having no deployment story

   • Even a small FastAPI project helps.

9. Using one CV for everything

   • The market punishes generic applications.

10. Not following up

• A polite follow-up after 7 days is normal.

Final Checklist Before You Apply#

Before sending your next Madrid ML engineer application, check this:

• Your CV title matches the role
• Madrid or relocation status is clear
• Work authorization is clear
• Python and SQL are visible
• Cloud tools are visible
• Deployment experience is visible
• Your best ML project has numbers
• Your GitHub links work
• Your LinkedIn matches your CV
• Salary expectations are ready
• You can explain every tool on your CV
• You tailored the first half of the CV to the job

If you can tick most of those, you are already ahead of many applicants.

Your Next Step#

Machine learning engineer jobs in Madrid in 2026 are competitive, but not impossible. The winners are usually not the people with the longest tool list. They are the people who show clear proof that they can build models, ship them, explain tradeoffs, and help the business make better decisions.

Before you send another application, run your CV through JobRise’s free checker and see what might block you in an ATS scan. Try it here: https://jobrise.io/en/free-ats-checker/