Scientific > Machine Learning Engineer
Salary National Average
| Low | Medium | High |
|---|---|---|
| 112360.0000 | 139730.0000 | 178870.0000 |
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Candidate Supply: 14,093 Job Openings: 5,229
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Short Description:
A Machine Learning Engineer is a specialized software engineer who focuses on designing, developing, and deploying machine learning models and algorithms. They work at the intersection of data science and software development, using their expertise to create predictive and data-driven solutions for a wide range of applications, from recommendation systems to autonomous vehicles.
Duties / Responsibilities:
- Collaborate with data scientists and domain experts to understand business problems and formulate machine learning solutions.
- Collect, clean, and preprocess data to make it suitable for model training.
- Develop, implement, and fine-tune machine learning models using programming languages like Python and frameworks such as TensorFlow or PyTorch.
- Evaluate model performance, refine algorithms, and optimize for efficiency and accuracy.
- Deploy machine learning models into production systems, often in collaboration with DevOps and software development teams.
- Maintain and update deployed models as new data becomes available or as business requirements change.
- Conduct research and keep up-to-date with the latest advancements in machine learning and artificial intelligence.
- Collaborate with cross-functional teams to integrate machine learning solutions into various applications.
- Document and communicate machine learning processes, results, and insights to non-technical stakeholders.
- Ensure data privacy, security, and compliance with relevant regulations when working with sensitive data.
Skills / Requirements / Qualifications
- Education: Bachelor's or master's degree in computer science, machine learning, artificial intelligence, or a related field.
- ML Techniques: Proficiency in machine learning algorithms, data structures, and statistical analysis.
- Programming: Strong programming skills in languages such as Python or R.
- ML Frameworks: Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Data Engineering: Knowledge of data engineering, including data extraction, transformation, and loading (ETL).
- Analytical: Strong problem-solving and analytical skills to address complex data and modeling challenges.
- Communication: Excellent communication and teamwork skills to collaborate with data scientists, engineers, and business stakeholders.
- Cloud Computing: Familiarity with cloud computing platforms and tools, such as AWS, Azure, or Google Cloud, can be advantageous.
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