Job File: machine learning engineer, what skills do you need?

Machine learning engineers are the architects of the AI revolution. At the heart of the data, they design, develop and deploy models that transform raw information into decisions. Take a behind-the-scenes look at this fascinating profession: the day-to-day tasks, skills and salary prospects that define it.

What is a machine learning engineer?

He is an expert in artificial intelligence. They create and train complex learning models. They develop AI solutions to solve business problems.

In practical terms, machine learning (ML) engineers transform a problem into an AI solution that works. They don't just do theory: they code, test and deploy models that have a real impact.

The ML engineering profession has come into its own with the rise of data and AI. It's a promising career choice, offering the opportunity to work on complex, innovative and exciting subjects in a wide range of sectors (health, finance, industry, tech, etc.).

Let's take a few examples.

With an e-commerce giant (such as Amazon or Cdiscount)

• The challenge: Increase sales by offering the right products to the right customers.
• The engineer's work : it designs and trains a recommendation system. This model analyses the shopping and browsing histories of millions of customers to predict what they might like.
• The end result: When you see the section "Products recommended for you" or "Customers who bought this item also bought...", this is the result of his work. Each personalised suggestion is generated by his model.

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In the automotive industry (Renault or Tesla)

• The challenge: make a car safer by enabling it to "see" and understand its environment.
• The engineer's work : it develops a computer vision model. He trains it with millions of road images and videos so that it learns to identify pedestrians, other cars, speed limits and road lines in real time.
• The end result: the automatic emergency braking system which activates itself when it detects a pedestrian. Or the lane keeping assistant, which corrects the trajectory. Its model analyses the camera images and makes the decision in a few milliseconds.

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At a bank (such as BNP Paribas or Société Générale)

• The challenge: combat fraud by detecting suspicious bankcard transactions among millions of daily transactions.
• The engineer's work : he builds a anomaly detection model. The model learns each customer's 'normal' spending habits (locations, amounts, times).
• The end result: you receive a SMS from your bank which blocked an unusual payment abroad. It was the ML engineer's model that identified this transaction as abnormal and triggered the security alert.

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For a streaming platform (such as Netflix or Spotify)

The challenge: keep users subscribed by constantly offering them content that they will love, so that they don't search and find nothing.

The engineer's work : it creates the customising the catalogue. The model analyses what you watched or listened to, at what time, and compares it with the profiles of millions of other users.

The end result: your playlist "Discoveries of the week on Spotify, which seems to know you by heart. Or the "Because you watched..." category on Netflix. It's its model that chooses and orders this content just for you.

Tasks of the machine learning engineer

What are its day-to-day tasks? Of course, they vary depending on the company and the sector, but the tasks remain the same.

• Understanding business needs. The engineer works with the project managers. They identify the problems to be solved. He then defines how AI can respond to them.
• Collect and prepare data. It gathers and cleans up the necessary data. This is a crucial stage for the quality of the model.
• Designing models. It chooses the right algorithms for each problem. They can use existing models or create bespoke ones.
• Training and experimentation. It trains the models on large volumes of data. It adjusts parameters to improve performance.
• Assess and validate. It measures model performance using precise metrics. It ensures that the model is robust and reliable.
• Go into production (MLOps). It optimises the model for deployment. It works with DevOps teams to integrate it into applications. Automation has become the norm.
• Monitor and maintain. Once deployed, the model is monitored. Any drop in performance must be detected. The model can be retrained with new data.
• Technology watch. AI is a fast-moving field. You need to keep up to date with the latest innovations and research.
• Documenting and sharing. He documents his work and communicates his results. He shares his knowledge with the rest of the team.

We often think that we spend our time creating revolutionary algorithms. The truth is that 80 % of the job is to clean and prepare the data.
Without good data, even the best model is useless.

Léa, engineer in a major group

Training and profile required

A 5 years of higher education is generally expected. Profiles come from engineering schools or universities. They specialise in computer science, applied maths or data science. A doctorate (or PhD) is a plus for R&D, but is not always compulsory.

In addition to their diploma, ML engineers often go on to experience in data science or software development. Young graduates will do well to accumulate AI projects (Kaggle competitions, internships in laboratories or AI start-ups, etc.) to stand out from the crowd. Professional qualifications in machine learning or the cloud (e.g. in the field of AI) will help them to stand out from the crowd. Google Cloud ML Engineercertification AWS in ML) can also strengthen a profile.

And finally.., an excellent level of technical English is essential.

Technical skills (know-how)

To excel, machine learning engineers need to combine a solid theoretical background with mastery of cutting-edge technological tools. Among the technical skills keys to acquire :

• Mathematics and statistics. Linear algebra and probability are fundamental. They help to understand how algorithms work.
• Deep learning & algorithms. Knowledge of neural networks (CNN, RNN, Transformers) is required. Knowledge of classical algorithms is also required.
• Frameworks and libraries. Controlling PyTorch or TensorFlow is essential. Tools such as Scikit-learn and Hugging Face are also widely used.
• Programming and software engineering. Excellent skills in Python are the norm. You need to know how to write clean, optimised and versioned code with Git.
• Data management. Mastering language SQL is crucial. Knowledge of big data (Spark) and NoSQL databases are a plus.
• MLOps and Cloud. Deploying models is a key skill. You need to know Docker and Kubernetes. Mastery of a cloud (AWS, Azure, GCP) is often required.

"The real challenge today is not just to create a precise model. It's putting it into production in a reliable and scalable way.
MLOps has become the sinews of war.

Marc, AI Architect

Behavioural skills (soft skills)

In addition to technical know-how, some personal qualities are essential for success in this demanding profession:

• Curiosity and creativity. You have to like to innovate and explore. Continuous learning is the key to success in this field.
• Thoroughness and problem-solving. Diagnosing a malfunctioning model requires patience. An analytical and methodical mind is essential.
• Adaptability. Technologies and projects are constantly evolving. You have to be flexible and willing to learn new things.
• Communication and collaboration. ML engineers work as part of a team. They must be able to explain complex concepts to non-specialists.
• Autonomy and organisation. They often manage several experiments at the same time. They must be organised and able to take the initiative.

What salary?

Remuneration is very attractive. It depends on experience, location and company.

• Junior (0-2 years) : about 45 000 € gross per year.
• Advanced (3-5 years) : between 55,000 and €65,000 gross per year.
• Senior (+5 years) : 75,000 and over90,000 for experts.

Salaries are higher in Paris. Large groups and tech companies often offer the best salaries.

Finally, highly sought-after skills (e.g. expertise in 3D vision, multilingual NLP, or experience in managing large-scale MLOps projects) may enable you to negotiate at the top end of the range.

Be that as it may, ML engineers are among the best-paid jobs in the digital sector from junior level upwards, and the prospects for salary progression are significant as you move up the skills ladder.

Outlook for development

The career prospects for a machine learning engineer are varied, given the high demand for this skill. After a few years, they can aim for more specialised or more strategic roles, for example :

• Senior AI Expert or Data Scientist. To specialise in a specific field (vision, NLP).
• AI Architect. To design the overall architecture of the company's AI systems.
• Project Manager or Manager. To lead a team and manage the strategy of AI projects.
• Entrepreneurship. To launch his own AI start-up.

The pros and cons of the job

Like all jobs, that of ML engineer involves very rewarding aspects and others more challenge on a daily basis.

✅ Highlights :

• Innovative and stimulating job. You're at the cutting edge of technology.
• Direct impact. Your models have a visible and measurable impact.
• High demand and excellent salary. Employability is guaranteed.
• Diversity of projects. The missions are varied and never monotonous.

❌ Weaknesses:

• Pressure to deliver. AI is sometimes seen as a miracle solution.
• Constant technological monitoring. You have to be constantly learning to keep up to date.
• High technical complexity. The job is intellectually demanding.
• Workload can be heavy. Deadlines can be tight.

"It's an incredible job, but you have to accept failure. For every model that works, there are ten that have failed before. It's perseverance that makes all the difference.

Sarah, ML Team Lead

Useful training for this profession

For those who want to start or improve their skills in this field, there are numerous training courses in data science and IA. In particular, ORSYS training can help you acquire or strengthen key skillsThe technical and behavioural aspects are described in this job description.

To find out more, visit our website at all our artificial intelligence training courses grouped together within our IA Academy. Armed with this knowledge, you'll have everything you need to become an accomplished machine learning engineer, ready to design the AI solutions of tomorrow!