Álvaro
Data Scientist
“In a climate this uncertain, Shakers gives you a layer of reassurance and confidence. What I like most, without a doubt, is the flexibility and getting to work on top-tier projects.”
Join ShakersShakers for expert talent
Build your career as a Machine Learning Engineer
Projects where the model and its production path are the engagement: what gets predicted, with which data and serving what. Shakers connects you with companies that arrive with the history and need the system, not with a course to complete.
Machine learning employment in Spain is full-time product and industry work, titled in English. The by-project route barely had a door: global marketplaces and a forum thread with decade-old rates.
This is not a training route: it is what gets delivered on a real engagement, what you must prove and where your territory ends.
- Higher-paying projects
- More continuity between projects
- Greater recognition for your experience
+3,000
Tech projects published on Shakers
+90
Tech, Product and Data specialisms are part of Shakers
6 months
Average length of a project with Shakers
4.7/5
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Amelia
AI Agent Developer
“I have been building AI agents for years. Thanks to Shakers I stopped chasing clients and started genuinely choosing which projects I wanted to work on. I work with some of the best companies in Europe, at my own rate, and I focus on what I am good at and what I love: building.”
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Luz
Backend Developer & Data Engineer
“Shakers adds a lot of value because it connects you with sharper projects, already filtered and better defined. It saves you a big part of the upfront work of understanding the client, quoting and weighing up whether it is worth it, and that means that as a contractor you can go far more directly to the opportunities that genuinely fit you.”
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Rubén
Software Developer
“Life as a contractor has a lot going on and it is not always easy. We like to focus on what we are good at, but you also have to manage clients, invoicing and go looking for work. For me, Shakers has been key because it does that prospecting for me, it gives the client confidence from the very first moment, and it lets us be part of a community with real enthusiasm.”
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Alejandro
AI Developer
“Shakers was key to getting my company off the ground: it gave me the flexibility to keep contracting while I built my next chapter. Without Shakers, I would have had to raise funding or push the project back.”
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Toño
Product Designer
“With Shakers I have landed recurring projects that give me peace of mind and room to grow professionally. It is not just about reaching quality clients: it is feeling that you have a network behind you, one that connects you with real opportunities and helps you move forward as a freelancer.”
Join ShakersWhat a Machine Learning Engineer decides every week
A Machine Learning Engineer designs, trains and deploys machine learning models: prepares the data, trains and evaluates with Python, PyTorch or scikit-learn, and takes the model to production with pipelines, containers and monitoring. The deliverable is a system that learns, not a report.
Their boundary: the data scientist explores and decides, the AI Engineer builds on foundation models and LLMs, and MLOps industrialises what another trained. The ML Engineer takes the model from data to system.
You decide what gets predicted and which metric judges it, train and evaluate with PyTorch or scikit-learn, and keep the model on its way to production: pipeline, MLflow, containers. You also decide what does not get trained: a case with no data to hold it up gets rejected with an argument. The bet is the business's; the system that learns is yours.
PythonPyTorchTensorFlowscikit-learnSQLSparkMLflowDockerKubernetesCI/CDAWS SageMakerVertex AIAzure MLAirflowKafkaHugging Face
Predictive modelsData pipelinesModel evaluationMLflowProduction servingFoundation models
Free to join, no hidden fees
How Shakers works for talent
Create your profile
Tell us who you are, what you can do, the projects you want to work on and what your availability and rate are.
Get your certification
We certify your experience and your use of AI so companies understand and trust what you bring.
Match with projects
We connect you with well-paid projects that fit your expertise and your preferences.
Projects that live up to your expectations
Work on real projects, with teams that need your expertise, without wasting time looking for opportunities.
Projects that fit you
Work with companies that need your stack and experience to take their projects to production.
Opportunities that come to you
Our AI matching connects your profile with the projects that fit your preferences.
You build. Shakers handles the rest
Set your own terms based on your seniority. We manage contracts, payments and paperwork.
What a Machine Learning Engineer delivers at Shakers
The engagement arrives with the model
The prediction the business expects, the history that exists and the system that is missing: a model problem, not a list of tools.
From data to serving
Metric defined, training evaluated and model serving: pipeline, MLflow and containers. Deliverables the team can run.
The library the case asks for
PyTorch, TensorFlow or scikit-learn by the problem, and MLflow to version. The tool serves the case, not the trend.
Counted models, not courses
Having trained with real consequences and knowing how to tell it: what you predicted, with which data and how you validated it.
Training is also discarding
Choosing between the model the client asks for and the data that will not hold the prediction. Sometimes not training wins.
Where your engagement ends
You build the model and serve it; the analysis of the past belongs to the data scientist, the data channel to the data engineer.
Frequently asked questions
Any other questions? Write to us and we will reply.
What does a Machine Learning Engineer do by project?
They build the system that learns: define the metric, prepare the data, train with Python and leave the model serving with pipeline and monitoring. One case, one period, one metric.
Can you make a living from contract machine learning?
The reference that dominates search is a thread with decade-old dollar rates. The by-project door barely existed here: today the Shakers ML cluster gathers 179 specialists by engagement.
How much to charge as a contract Machine Learning Engineer?
The mechanism marks it: seniority, sector, type of model and billing by project or by days. The comparison with salaried employment lives in the blog guide; your budget closes per engagement.
Does the role still exist with foundation models?
Yes: whoever trained from zero today also builds on foundation models, with fine-tuning and RAG. Employment already titles it that way ('GenAI/LLM/RAG'); the craft covers both halves.
Is there enough engagement in Spain for the role?
Employment sends the signal: 329 active tech ads in Spain mention machine learning engineer (Shakers market analysis, n=31,957, September 2026). The by-project route was missing.
How do I get in without a contact network?
By getting certified: the process certifies your practice and the collective brings the engagements. Your work is showing models with real consequences; matching each project to your profile is ours.
The daily shape of the craft
The history nobody cleaned, the metric nobody defined and the prototype that stayed in the notebook. Less course theory, more models serving, evaluation documented.
The market titles you in English
Employment in Spain is full-time product and industry work, titled in English: 329 active tech ads mention machine learning engineer, from Wallapop to Airbus (Shakers market analysis, n=31,957, September 2026).
The foundation layer does not replace you
Much of the work that started from zero today builds on foundation models: fine-tuning, embeddings, RAG. The craft stays; the starting point moves, and mastering both halves doubles your value.
Arriving from another craft
Frequent doors: the data scientist who started shipping, or the Python backend who ended up caring for the model. If you already train and evaluate outside the course, half the battle is won.
Who you share the model with
A model does not live alone: whoever explores the data and whoever operates production cross your path in every engagement.
Data Scientist
The data scientist explores and decides; the ML Engineer builds and ships. Ads fuse them into dual titles; each engagement arrives with the boundary written.
AI Engineer
The other frontier: LLMs, agents and RAG on foundation models. When the engagement asks for the generative layer besides the own model, their row builds it.
Data Engineer
The supply of the model: Spark, Airflow and the pipeline that leaves the history ready. Without prepared data, your system learns nothing; their row builds it.
Python Developer
The language of the craft: pandas, APIs and automation. When the model already serves and the application around it is missing, their row builds the product.
DevOps Engineer
Whoever operates the platform that serves your model: Kubernetes, Terraform and CI/CD. When the engagement grows towards infrastructure, their row joins.
Boundaries between these roles get agreed per project: the job title does not set them.
Take the next step with Shakers
Want to build models that serve?
Pass Shakers certification and choose which models get your judgement.
