Machine Learning or Deep Learning Engineer : job description
A Machine Learning (ML) or Deep Learning (DL) Engineer is an AI professional who designs, builds, and deploys intelligent systems that learn from data. Working at the intersection of data science, software engineering, and artificial intelligence, they develop predictive prototypes, algorithms, and neural networks used in real-world applications such as computer vision, natural language processing, recommendation systems, and autonomous technologies. Machine Learning Engineers typically focus on statistica examples and classical ML techniques, while Deep Learning Engineers specialize in advanced architectures like convolutional and recurrent neural networks. Their role includes data preparation, model training, performance optimization, and production deployment, ensuring extensible, reliable, and high-performing AI solutions that turn data into actionable insights.
What does a machine learning engineer do?
A machine learning engineer designs, builds, and deploys mechanisms that enable computers to learn from data and make predictions or decisions without being explicitly programmed. Their primary role is to turn machine learning prototypes into reliable, extensible machine learning systems used in real-world applications such as recommendation engines, fraud detection, and autonomous systems.
Machine learning engineers combine software development skills with data analysis and data mining to prepare large datasets, select appropriate machine learning algorithms, and optimize model performance. They work closely with data scientists to implement approaches like supervised learning, unsupervised learning, and reinforcement learning, depending on the problem being solved.
In the broader field of artificial intelligence, machine learning engineers are responsible for training models, validating results, deploying them to production, and monitoring performance over time. They also ensure prototypes are efficient, secure, and maintainable. Ultimately, a machine learning engineer bridges the gap between theoretical AI research and practical, production-ready intelligent systems that power modern technology.
What's the Difference Between Machine Learning and Deep Learning?
To properly understand the profession's scope, it's essential to distinguish two interconnected concepts. Machine learning is a subdomain of artificial intelligence that gives computers the ability to learn from data without being explicitly programmed for each task. For its part, deep learning is a specific branch of machine learning that uses complex artificial neural networks inspired by the human brain. These neural networks, composed of many layers, excel particularly in processing unstructured data such as images, sound, or natural language text.
Criterion | Machine Learning | Deep Learning |
Data type | Often structured data, requiring significant feature engineering | Structured or unstructured (images, texts, sounds) |
Models | More classical algorithms (regression, decision trees, SVM) | Deep neural networks (CNN, RNN, Transformers) |
| Data volume | Can function with more modest datasets | Requires very large data volumes |
Computing needs | Moderate to significant | Very high, requiring GPUs/TPUs |
Usage examples | Basic recommendation system, fraud detection, predictive analysis | Computer vision, natural language processing (chatbot, translation), video games |
What Are the Main Missions of a Machine Learning Engineer?
A machine learning engineer designs, develops, trains, and deploys machine learning protoypes to solve complex business problems from data. Their role is at the intersection of data processing, software development, and MLOps.
Main Missions of a Machine Learning Engineer
- Data preparation and processing : the machine learning engineer works closely with data analysts and data engineers to ensure data is reliable, accessible, and usable. This includes: data collection and access, data cleaning and transformation, feature engineering to improve model performance
- Model development and training : they design and implement machine learning and deep learning algorithms, often based on upstream work defined by a data scientist. Their responsibilities include: selecting examples and tuning hyperparameters, training models in distributed computing environments, optimizing performance, scalability, and training time
- Model evaluation and validation: machine learning engineers assess the quality, robustness, and reliability of models using: validation and test datasets, appropriate performance metrics (accuracy, recall, F1-score, AUC, etc.), bias analysis and overfitting risk detection
- Deployment and production (MLOps): a critical responsibility is deploying machine learning models into production. This involves: containerization with Docker, orchestration with Kubernetes, using MLOps tools such as MLflow or Kubeflow, building APIs to integrate models into information mechanisms
- Monitoring, maintenance, and continuous optimization : nce deployed, the machine learning engineer ensures: real-world performance monitoring detection of data drift and schemas drift, automated retraining pipelines to maintain prediction quality
In What Environments Do They Work?
Whether optimizing the supply chain, personalizing customer experience on social networks, or developing facial recognition systems, the machine learning engineer's expertise is mobilized wherever it's about creating intelligence from data.
They're found for example in innovative startups (tech, fintech, healthtech, etc.), in research and development teams at large corporations (aerospace, automotive, energy, etc.), in banking-insurance sectors, healthcare for diagnostic assistance, Industry 4.0, or in public and private research laboratories.
What's the Difference with a Big Data Architect or AI Engineer?
Several complementary data science professions are often confused.
Profession | Main Role | Focus | Tools and Skills |
| Data Engineer | Builds and maintains data pipelines (big data) | Infrastructure, databases, ETL | SQL, Spark, Hadoop, Kafka, Cloud (AWS/GCP/Azure) |
| Data Scientist | Explores data, builds and tests statistical models | Analysis, modeling, research, decision-making | Python, R, statistics, Scikit-learn, business analysis |
| Machine Learning Engineer / AI Expert | Broader term, can cover ML Engineer or specialize in complex AI systems (NLP, vision) | Advanced AI, system integration | Deep Learning, neural networks, natural language processing |
What Skills and Qualities Are Required?
The machine learning engineer must combine specialized technical expertise with personal qualities that ensure work effectiveness and impact on company performance.
Technical skills, called hard skills, form the concrete foundation of machine learning expertise:
- Programming languages: Python mastery is essential. Knowledge of R, Java, Scala, or C++ is a plus.
- Mathematics & Statistics: Linear algebra, differential calculus, probabilities, and inferential statistics form the theoretical foundation for understanding algorithms.
- Machine Learning & Deep Learning: In-depth knowledge of algorithms (supervised, unsupervised, reinforcement learning), neural networks (CNN, RNN, Transformers), modeling techniques, and frameworks (TensorFlow, PyTorch).
- Software Engineering & DevOps: Development skills (git, tests, CI/CD), and especially MLOps for deployment (Docker, Kubernetes, cloud services).
- Data Processing: Mastery of SQL and data manipulation tools (Pandas, NumPy, Spark).
Human qualities, called soft skills, are the essential catalyst for this technical expertise:
- Curiosity and technology monitoring: The field evolves very quickly, like generative AI
- Rigor and analytical mind: To analyze efficiency and debug complex systems
- Problem-solving ability: Solution-oriented and pragmatic
- Communication and teamwork: Essential for working with data scientists, developers, and project managers
What Skills to Develop to Progress?
For a junior machine learning engineer aiming to reach a mid-level or senior role, specialization combined with skill broadening is essential. Mastering MLOps has become a core requirement, as companies increasingly expect engineers to deploy, monitor, and scale machine learning models in production.
Advanced specializations such as Natural Language Processing (NLP), Large Language Models (LLMs), computer vision, and reinforcement learning open access to high-impact, cutting-edge AI projects. These domains are especially valued in sectors like generative AI, autonomous mechanisms , and intelligent automation.
Strong cloud expertise using platforms such as AWS SageMaker, Google Cloud Vertex AI, or Azure Machine Learning along with a solid understanding of distributed mechanisms and big data architectures, is a major differentiator. Together, these skills form the foundation required to progress toward a senior machine learning engineer position.
How to Become a Machine Learning Engineer?
To become a machine learning engineer, start with strong foundations in math (linear algebra, probability) and programming (Python, SQL). Earn a degree in computer science, data science, AI, or engineering or equivalent experience.
At entry level, focus on machine learning fundamentals: supervised/unsupervised learning, feature engineering, and training data preparation. Learn key tools like scikit-learn, TensorFlow, PyTorch, and basic MLOps (Git, Docker, model deployment).
Take online courses or certifications, build real projects, and practice on Kaggle or open datasets. A typical career path goes from junior ML engineer → ML engineer → senior/lead or specialist roles.
To become a professional machine learning engineer, combine theory, hands-on experience, and business problem-solving. Employers look for proven projects, clean code, and the ability to deploy models in production.
Several pathways of excellence allow reaching this level and acquiring necessary specialization, and typical training programs to access the profession are structured as follows:
Training | Typical Pathway | Strengths and Prospects |
| Bachelor (bac+3) | Bachelor's in computer science, mathematics, or statistics | Acquisition of fundamentals. Requires further studies. |
| Master (bac+5) | Master's in artificial intelligence, data science, computer science with machine learning specialty | Advanced theoretical depth |
| Engineering or business school (bac+5) | General or specialized engineering cycle in computer science/data with final-year specialization | Project approach, strong technological culture, and close ties with companies |
| Dual degree/MSc | Combination of engineering school and business school, or specialized MSc | Hybrid profile, combining technical expertise and business vision, highly valued |
Master in Management Program
Among the most prestigious programs, the Master in Management Program offered by emlyon business school, with a major in Data Science & Artificial Intelligence, trains profiles capable of managing data-driven decision-making.
Master in Management Program Brochure
Master in Data Science & Artificial Intelligence Strategy
For even more specialized training, the school also offers the Master in Artificial Intelligence & Data Science. This program constitutes an excellence pathway specifically designed to train tomorrow's experts and engineers in machine learning, deep learning, and big data.
Master in Data Science & AI Strategy Brochure
These programs integrate practical application of theoretical concepts through concrete projects and business cases, allowing students to build an operational profile highly attractive in the job market.
What Is the Salary of a Machine Learning Engineer?
Machine learning engineers enjoy highly attractive salaries, reflecting strong demand and a shortage of qualified professionals. Compensation varies by experience, location, industry, and company size.
Experience Level | Gross Annual Salary Range | Context |
Junior
| €40,000 – €55,000 | Recent graduates from top schools |
Mid-level
| €55,000 – €75,000 | Experience with production systems |
Senior / Lead ML Engineer |
€75,000 – €100,000 |
Technical leadership, deep expertise |
What Factors Influence Salary?
Beyond experience, compensation depends on several key factors:
- Advanced technical expertise: LLMs, NLP, MLOps, and advanced software engineering significantly increase value
- Industry sector: Finance, pure tech, defense, and healthcare offer the highest salaries
- Company size and type: Startups may offset lower base salaries with equity or stock options
- International scope: Fluent English is often mandatory for the highest-paying positions, in France or abroad
What Is the Expected Salary of a Machine Learning Engineer?
- ML Engineers benefit from strong career expansion opportunities
- The role offers long-term employment stability and high demand across industries
- ML Engineering remains one of the most lucrative and in-demand jobs in AI
Career Growth and Opportunities
Career progression for a Machine Learning Engineer is highly dynamic and diverse, reflecting the growing maturity of the AI sector. A typical career path follows a clear progression: Junior Machine Learning Engineer → Machine Learning Engineer → Lead ML Engineer or AI Architect → AI Manager or Head of Data → CTO or Research Director.
Career opportunities generally fall into three main tracks.
The technical expert track focuses on advanced model design, large-scale ML systems, and architecture, leading to roles such as Lead ML Engineer or AI Architect.
The management and strategy track combines technical expertise with leadership and business impact, evolving toward AI Manager, Head of Data, or executive positions.
The research and innovation track targets theoretical and cutting-edge AI development, with potential roles such as Research Director.
As the AI industry continues to expand rapidly, new positions constantly emerge, ensuring strong long-term employability and career growth.
Which Companies Hire Machine Learning Engineers?
Machine learning engineers are recruited across all industries, including:
- Big Tech companies (Google, Meta)
- CAC 40 corporations (TotalEnergies, L’Oréal, Sanofi)
- Defense and aerospace (Thales)
- Consulting firms (Capgemini, Accenture)
- A wide range of innovative startups
The Job Market and AI / Machine Learning Sector Trends
The job market for ML engineers is structurally tight, with demand far exceeding supply of qualified profiles. Job offers have experienced double-digit growth in recent years, as shown by Indeed or Apec studies.
This dynamic accompanies strong trends reshaping sought-after specializations:
- Explosion of generative AI (LLMs, advanced chatbots) creates a rush toward natural language processing experts and prompt engineering
- Industrialization via MLOps becomes the norm, making implementation, monitoring, and model lifecycle management skills crucial
- Responsible AI (ethics, security, bias reduction) and embedded AI (edge computing) emerge as critical domains, giving birth to new specialist functions
What are the challenges faced by machine learning engineers?
Machine learning engineers face several complex challenges throughout the lifecycle of AI systems. One major issue is handling large, messy, and constantly changing data while ensuring accuracy and reliability. Building scalable models that maintain strong performance in real-world environments is another key challenge, especially when systems must operate under latency or cost constraints. Proper evaluation of models is difficult, as metrics may not fully reflect business or ethical impacts. After deployment, continuous monitoring is required to detect model drift, bias, or performance degradation. Engineers must also integrate feedback loops to keep models improving over time. Balancing experimentation with production stability, ensuring reproducibility, and aligning models with real user needs make machine learning engineering a technically demanding and constantly evolving field.
Key points on the Machine Learning Engineer Profession
In summary, the machine learning engineer is the architect of operational AI, a hybrid professional who combines data science and software engineering to create value at scale. This high-demand profession offers a future rich in challenges and opportunities. Here are five key points to remember:
- Key industrialization role: The machine learning engineer is the essential link transforming an experimental model into an operational, robust, and scalable solution.
- Hybrid skills: This profession requires a clever mix of data science, software engineering, and soft skills to solve complex business problems.
- Demanding training: A bac+5 like MiM or Master in Data Science and Artificial Intelligence Strategy of emlyon is the expected level. Practice through concrete projects is essential.
- Attractive compensation: Salaries, especially from experienced level, are very competitive, with strong evolution based on expertise and sector.
- Exceptional future prospects: Facing demand exceeding supply, career prospects and advancement are vast and promising in this field in perpetual innovation.