Machine Learning Engineer Job Description Template
Use this Machine Learning Engineer job description template to advertise the open roles for free using Longlist.io. You can use this template as a starting point, modify the requirements according the needs of your organization or the client you are hiring for.
Job Brief
We are looking for a Machine Learning (ML) Engineer to help us create artificial intelligence products.
Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. If you also have knowledge of data science and software engineering, we’d like to meet you.
Your ultimate goal will be to shape and build efficient self-learning applications.
Responsibilities
- Study and transform data science prototypes
- Design machine learning systems
- Research and implement appropriate ML algorithms and tools
- Develop machine learning applications according to requirements
- Select appropriate datasets and data representation methods
- Run machine learning tests and experiments
- Perform statistical analysis and fine-tuning using test results
- Train and retrain systems when necessary
- Extend existing ML libraries and frameworks
- Keep abreast of developments in the field
Requirements
- Proven experience as a Machine Learning Engineer or similar role
- Understanding of data structures, data modeling and software architecture
- Deep knowledge of math, probability, statistics and algorithms
- Ability to write robust code in Python, Java and R
- Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
- Excellent communication skills
- Ability to work in a team
- Outstanding analytical and problem-solving skills
- BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus
What does Machine Learning Engineer do?
A machine learning engineer typically has the following day-to-day responsibilities:
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Data preprocessing: Clean and preprocess datasets to ensure they are suitable for machine learning models.
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Model development: Develop and fine-tune machine learning models such as linear regression, decision trees, neural networks, or support vector machines based on the problem at hand.
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Feature engineering: Identify and extract relevant features from datasets to improve the performance of machine learning models.
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Model training and evaluation: Train machine learning models using training datasets and evaluate their performance using appropriate evaluation metrics.
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Hyperparameter tuning: Optimize model performance by tuning hyperparameters such as learning rate, batch size, regularization, or architecture of the neural network.
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Model deployment: Deploy machine learning models into production systems and ensure they are scalable, efficient, and maintainable.
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Monitoring and maintenance: Monitor the performance of deployed models, identify and solve issues or bugs, and periodically retrain models using updated data.
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Collaboration and communication: Collaborate with cross-functional teams, such as data scientists, software engineers, and business stakeholders, to understand requirements and translate them into machine learning solutions. Communicate the results and findings to stakeholders in a clear and understandable manner.
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Staying up-to-date: Keep up with the latest research papers, advancements, and trends in machine learning to continually improve skills and stay ahead in the field.
It's worth mentioning that the specific tasks may vary depending on the organization, project, and the level of seniority of the machine learning engineer.