Machine Learning Engineer

We are Humans.tech.

We create intelligent interfaces that solve complex problems and improve experiences, in addition to being, of course, beautiful! We collaborate with global companies such as Airbnb, Amazon, Angelini… and we are only at the letter A.

In 10 years, we have launched over 700 digital products, we work 70% of the time with global clients between Europe and the United States, and we have offices in Frosinone and San Francisco. We are among the 1000 fastest-growing European companies for Financial Times & Statista and among the top 5 companies to work for in Italy for Gen Z according to Great Place to Work 2026.

We have been working with AI since 2015, not since it became a trend. Our Partners are called OpenAI, Meta, AWS, Translated.

We are not the usual consultancy. We support product companies and companies that want to become them, and we truly build the products: from analysis to architecture, from code to delivery. It’s like working within a product team, only we are the product team.

Who we are looking for

We are not looking for someone who knows how to develop. We are looking for someone who gets bored doing it in an ordinary way.

A senior person. Truly senior, not just senior on LinkedIn.

Someone who has developed, maintained, and scaled high-traffic systems and not just looked at them from the outside, not touched them in pieces. Someone who has put products into production and closes the loop, from training to serving, not just trains a prototype and passes the ball.

An AI-first person: they conceive the project architecture with their own mind, then use the right tools to build, redesign, and accelerate it. AI is a lever, not a shortcut.

Here's how it works: we give you the opportunities. You take them, understand them, contextualize them. You move quickly towards solutions, make decisions, and bring them to production. Without creating friction.

How we think, who you will work with

We work towards objectives. This makes us flexible and very demanding. We push every person on the team beyond their current limits because only then can we achieve results that exceed initial expectations.

We don't want you to have ready-made answers. We want you to know how to ask the right questions and have the critical thinking to dig into them.

You will join a cross-functional team: a fully empowered version of a cohesive group, transversal in knowledge and vertical in skills. Software Engineers, ML/AI specialists, Product Designers, all in the same room, whether physical or virtual.

Here, we support the best idea, not our own idea. We ask you to bring many, and to let go of yours when a more solid one emerges. "We've always done it this way" is not an acceptable answer.

We cultivate excellence, but we know that perfection, in human processes, is a lie. People stay here: this means stable teams, low turnover, real continuity on projects. It's our best indicator.

⚙️ What you need to know how to do

• Solid Python. Clean and tested code, comfortable with Git and CI/CD (GitHub Actions). Production-ready doesn't scare you: you master the fundamentals.
• ML Algorithms, supervised and unsupervised. You know the main ones and know which one is needed for which problem, and you take a model from dataset to useful result. Data preparation and feature engineering (Pandas, NumPy) are everyday tasks.
• Deep learning with PyTorch and/or TensorFlow. You train and optimize models (CNNs, RNNs, Transformers) and have applied them to NLP or Computer Vision (BERT, GPT, YOLO). You start from solid foundations and adapt them to the problem.
• Mathematical and statistical fundamentals. Linear algebra, probability, statistics. You need them to read metrics, understand if a model is working, and notice when something is wrong.
• MLOps principles and putting into production. ML pipelines for deployment and monitoring, Docker in daily workflow, serving on the cloud (AWS, GCP, or Azure). You have seen models reach production and know how to navigate their lifecycle.
• Data engineering. SQL and NoSQL, queries and pipelines to feed training and inference. You keep an eye on data quality.
• Model evaluation and monitoring. Serious metrics (accuracy, precision, recall, F1; MAE, RMSE), confusion matrices, overfitting and underfitting recognized on the fly. You notice when a production model is degrading.
• Agentic coding tools in the daily workflow.
• Professional English, written and spoken.

Plus

• Kubernetes and orchestration, autoscaling, and inference cost optimization.
• Vector databases (e.g., Qdrant) and/or graph databases (e.g., Neo4j).
• LLMs, RAG, and initial experiments with multi-agent orchestration (LangGraph, AutoGen, CrewAI); interest in GenAI.
• Advanced observability: drift detection, retraining, and model versioning (MLflow, DVC).
• Data workflows (Apache Airflow), open-source contributions, or Kaggle challenges.
• Native-level English.

Who won't work

• You need hyper-detailed specs to get started. With us, the problem is handed to you: you model it yourself.
• You expect micromanagement, or you seek an environment with processes designed to protect underperformers.
• You only see your small part. If you think "this is my task, the rest is not my problem," you block everyone.
• You are slow to decide. Waiting for validation on every choice costs us time. Ownership is taken, not delegated.
• You need months to "find your rhythm." You are not here to study, but to validate.

How we work

Full remote? It works. If you are at the level we are looking for in this ad. What matters is the quality of the output, not where you live.

However, if you can and want to come, hybrid is the path we recommend: the team has 70 people, and being in the same room accelerates growth much more than any call.

If you want to relocate, we'll help you: relocation bonus of €3K.

❤️ What's at stake

• Full-time contract.
• Annual Salary (RAL) from €36,000 to €42,000+. The "+" is not graphic, it's opportunity.
• Projects in international markets with business KPIs.
• Meal vouchers (full-ticket).
• Healthcare: health insurance + access to Unobravo.
• Wealthcare: Corporate Benefits + Starting Finance.
• Top-of-the-line hardware: MacBook Pro, of course.
• Personalized growth plan, coaching, and on-the-job mentoring.
• National and international team building events.
• Performance-related bonuses.
• Relocation bonus of €3,000 (if you wish).

And when you're in the office, the spaces are designed to be enjoyable: a fully equipped gym with changing rooms, a paddle tennis court, professional simulators, a bar, a relaxation area, a dining area, dedicated parking. Things that make the day sustainable, pleasant, and human: ➡️ watch the video ⬅️

How we get to know each other

Three steps, no fluff:

1. Introductory interview (30 min): a chat.
2. In-depth interview (30 min): culture, working methods, benefits.
3. Pair programming (90 min): we work together on a real ML problem.

Fast, because time is the first asset. Yours, and ours.

If you are waiting for the perfect moment to apply: now is the time. Write to us.

This announcement is aimed at candidates of all genders (D.Lgs. 198/2006).

The RAL range reflects the high relevance of the requested role.