A machine learning engineer is a specialist who designs, builds, and deploys machine learning models. They bridge data science and software engineering to develop intelligent systems capable of learning patterns.
In this article, you’ll learn how to write an effective machine learning engineer job description, including key responsibilities and required qualifications. You’ll discover the key differences between junior and senior ML roles and get customizable templates for each seniority level. So, let’s get started!
Machine learning engineers are programmers who develop AI systems using large datasets to create algorithms that learn and predict outcomes.
In practice, this role handles the entire machine learning pipeline. Your ML engineer will collect and organize data, run experiments to find the best algorithms, and continuously monitor and fine-tune models’ performance.
As for the requirements, most companies look for experience with Python, Java, R, and Scala programming languages, plus TensorFlow or PyTorch frameworks. In fact, the exact mix of machine learning skills depends on your industry focus and existing data science infrastructure.
Below you’ll find a complete template with all essential sections for your ML engineer job description. The template uses placeholders marked with {{ }} for easy customization. Simply replace these with your specific requirements and tech stack details.
Job title: {{ Junior / Middle / Senior / Lead }} Machine Learning Engineer
Location: {{ City, Country / Remote }}
Employment type: {{ Full-time / Part-time / Contract }}
We are seeking a skilled Machine Learning Engineer to join our team and work on {{ computer vision / NLP / Generative AI / robotics / other application area }} solutions. This role involves {{ 1-3 key responsibilities}}.
You’ll work directly with our {{ team type}} to boost system efficiency and create next-generation solutions. This role includes hands-on R&D and practical applications of machine learning in {{ your industry/domain }}.
▪ Design and develop machine learning models using {{ TensorFlow / PyTorch / scikit-learn / Keras }} frameworks and libraries
▪ Collect, preprocess, and analyze large datasets for model training and validation
▪ Build and maintain data pipelines and ETL processes to feed data into models
▪ Integrate machine learning models into production systems and applications
▪ Monitor model performance and accuracy, and iterate on improvements to algorithms
▪ Optimize model inference and training performance
▪ Write and maintain tests and documentation for ML code
▪ Collaborate with {{ data scientists / data engineers / software developers / product managers }} to align machine learning solutions with business goals
▪ {{ Bachelor’s / Master’s / PhD }} degree in Computer Science, Engineering, Mathematics, or equivalent experience
▪ {{ x }}+ years in machine learning engineering or related field
▪ Proficiency in Python (and ideally one other language such as {{ Java / C++ / Scala / R }})
▪ Hands-on experience with {{ TensorFlow / PyTorch / Keras / scikit-learn / pandas / NumPy }}
▪ Solid understanding of machine learning algorithms and model evaluation techniques
▪ Experience with {{ AWS / GCP / Azure }} cloud platforms for ML workloads
▪ Experience with data preprocessing and feature engineering
▪ Familiarity with Git-based workflows
▪ Exceptional mathematical skills, high attention to detail
▪ Excellent communication skills and {{ other important soft skills in your tech team }}
▪ Experience with big data processing frameworks (Apache Spark, Hadoop)
▪ Experience with {{ generative AI / LLMs / computer vision / NLP / robotics }} in {{ domain specialization }}
▪ Familiarity with containerization technologies (Docker) and orchestration platforms
▪ Competitive salary based on experience and expertise
▪ Paid vacation ({{ x days }} per year) and paid sick leave
▪ {{ Health insurance / Wellness stipend }}
▪ {{ Professional development budget / Training courses }}
▪ {{ Modern hardware / Home office setup }}
{{ Send your resume to careers@company.com / Apply through our careers page }}
Include links to your GitHub profile, any relevant ML projects, or code examples.
Python Developer Job Description
Prompt Engineer Job Description
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A junior machine learning engineer is an entry-level role that requires around 1 year of commercial experience or a relevant bachelor’s or master’s degree in Computer Science, Mathematics, or Data Science.
Junior ML engineers are responsible for supporting model development and data pipeline creation under senior guidance. The position requires Python programming skills, basic ML frameworks like scikit-learn or TensorFlow, and strong foundational knowledge in statistics and linear algebra.
When hiring for this level, focus on candidates who demonstrate eagerness to learn, solid programming fundamentals, problem-solving, and collaborative skills.
Now, let’s look at an entry-level machine learning engineer job description template that captures these junior responsibilities and skills.
Job title: Junior Machine Learning Engineer
Location: {{ City, Country / Remote }}
Employment type: {{ Full-time / Part-time }}
We’re looking for a Junior Machine Learning Engineer to join {{ Company name }}, building intelligent solutions for business automation. You’ll work alongside experienced ML engineers and data scientists in a collaborative environment.
This role offers hands-on experience with the complete ML pipeline from data preprocessing to model deployment, with direct mentorship and growth opportunities.
▪ Support the development and training of machine learning models under guidance
▪ Clean and organize datasets to prepare them for training and testing phases
▪ Conduct data analysis to assess data quality and verify data readiness for machine learning projects
▪ Build and refine data processing workflows
▪ Share key insights, analysis updates, and experiment findings with stakeholders
▪ Collaborate with team members to integrate machine learning solutions into applications
▪ Document code and processes, and participate in code reviews to improve code quality
▪ Stay updated on the latest advancements in machine learning and actively learn new tools
▪ Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or related technical field, or comparable professional experience
▪ 1+ years of experience (including internships or academic projects) in machine learning
▪ Proficiency in Python with experience in pandas, NumPy, scikit-learn, TensorFlow, or PyTorch
▪ Basic understanding of data preprocessing, feature engineering, and model validation methods
▪ Solid foundation in mathematics (linear algebra and statistics)
▪ Experience with data manipulation and analysis
▪ Familiarity with SQL for data querying and database interactions
▪ Familiarity with version control (Git) and collaborative coding workflows
▪ Strong analytical and problem-solving skills, with a willingness to learn and work collaboratively in a team
▪ Upper-intermediate level of English communication skills (written and spoken)
▪ Proficiency in additional programming languages for data analysis ( R or Scala)
▪ Basic understanding of cloud platforms (AWS, Azure, or GCP preferred)
▪ Experience with distributed data processing frameworks (e.g., Apache Spark)
▪ Background in specialized domains (computer vision, NLP, robotics, AI)
▪ Knowledge of data visualization tools for analysis and presentation
A senior machine learning engineer is an experienced professional with typically 5+ years of expertise in developing and deploying ML models.
Senior ML engineers lead complex projects, make architectural decisions, and often serve as technical leads for AI initiatives. They’re responsible for designing end-to-end ML systems, establishing MLOps practices, optimizing model performance, and mentoring junior team members.
These machine learning engineer roles usually demand deep expertise in multiple ML frameworks and libraries, cloud-native ML deployment, and a set of fundamental skills in data engineering and software architecture.
In fact, senior ML engineers need both technical mastery and leadership skills to guide teams through intelligent system implementations.
Now, let’s take a look at an example of a senior machine learning job description.
Job title: Senior Machine Learning Engineer
Location: {{ City, Country / Remote }}
Employment type: {{ Full-time / Part-time / Contract }}
We’re seeking a Senior Machine Learning Engineer to lead the design, deployment, and optimization of production-scale ML systems. You’ll own complete ML projects from conception through production deployment.
This role offers the opportunity to shape our technical direction and mentor engineering teams. You’ll collaborate directly with product and engineering leadership to translate business requirements into large-scale machine learning solutions.
▪ Lead end-to-end machine learning projects from data exploration and model development to deployment and monitoring
▪ Design and architect ML infrastructure, including containerized model serving and data pipelines
▪ Build MLOps practices, including CI/CD pipelines, model monitoring, A/B testing frameworks, and automated deployment systems
▪ Define technical specifications and SLA requirements for ML services
▪ Mentor and guide junior ML engineers, including code reviews
▪ Lead incident response and performance optimization efforts
▪ Establish data governance and model risk management frameworks
▪ Master’s or PhD in Computer Science, Machine Learning, Mathematics, Physics, or related field
▪ 5+ years of experience as a machine learning engineer or similar
▪ Strong programming skills in Python and proficiency in additional languages (such as C++ or Java)
▪ Deep expertise with modern ML/DL frameworks (PyTorch, TensorFlow, XGBoost, scikit-learn) and LLM technologies (HuggingFace)
▪ Strong background in statistical analysis, model evaluation, A/B testing, and performance metrics interpretation
▪ Proven experience with MLOps infrastructure, including Docker, Kubernetes, model serving platforms (Triton, FastAPI), and CI/CD pipelines
▪ Hands-on experience with cloud platforms (AWS/GCP/Azure services)
▪ Proven ability to lead technical projects and mentor other engineers, and excellent problem-solving skills
▪ Experience with generative AI, conversational agents, retrieval-augmented generation (RAG), or reinforcement learning from human feedback (RLHF)
▪ Specialized domain expertise (e.g., computer vision, natural language processing, recommendation systems, or robotics)
▪ Knowledge of vector search technologies (Pinecone, FAISS, Weaviate) and embedding techniques
▪ Experience with distributed computing frameworks (Apache Spark, Ray) and big data processing
Don’t feel obliged to use all of these sections. The examples above are comprehensive, but can be tailored to suit your specific needs and company culture.
The key is being authentic about what you offer while clearly communicating the technical challenges that will excite ML engineers. Remember, the best candidates have multiple options, and your machine learning engineer job description needs to show why your opportunity is worth their time and expertise.
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A machine learning engineer is a technical specialist who builds and deploys ML systems that learn from data. They combine software engineering skills with deep knowledge of statistical modeling and algorithms to create production-ready ML solutions.
Often, ML engineers can work in different verticals, covering AI, data science, data engineering, computer vision, NLP, MLOps, and robotics.
Machine learning engineers develop algorithms that enable computers to learn patterns from data without explicit programming. They build data pipelines, train models, optimize performance, and deploy ML systems to production environments. Their daily work involves writing Python code, processing large datasets, fine-tuning model parameters, and monitoring already deployed models.
First of all, proficiency in Python programming, experience with ML frameworks like TensorFlow or PyTorch, understanding of statistics and linear algebra, and knowledge of cloud platforms. Additionally, they require data preprocessing skills, problem-solving abilities, attention to detail, and strong communication skills.
Most AI/ML job descriptions require at least a bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Many employers prefer candidates with a master’s degree or a PhD, especially for senior roles. However, equivalent practical experience through internships or self-directed learning can sometimes substitute for formal education.
