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Machine Learning Engineer
- New York, New York, United States
- New York, New York, United States
About
Key Responsibilities
Research, design, and implement machine learning models and algorithms to address business challenges and opportunities.
Collect, preprocess, and analyze data from various sources to extract meaningful insights and features.
Develop and maintain scalable machine learning pipelines for training, evaluation, and deployment of models.
Collaborate with software engineers to integrate machine learning solutions into production systems and applications.
Evaluate and benchmark different machine learning techniques and frameworks to optimize performance and efficiency.
Stay up-to-date with the latest advancements in machine learning research and technology to drive innovation and best practices within the team.
Qualifications
Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Engineering, Mathematics, Statistics, or related field.
Proven experience in developing and deploying machine learning models in real-world applications.
Proficiency in programming languages such as Python, R, or Java, as well as experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, or scikit‑learn.
Strong understanding of machine learning algorithms, techniques, and best practices, including deep learning, supervised/unsupervised learning, and reinforcement learning.
Experience with data preprocessing, feature engineering, and model evaluation/validation techniques.
Excellent problem‑solving skills and the ability to work independently and collaboratively in a team environment.
Effective communication skills with the ability to translate complex technical concepts to non‑technical stakeholders.
Preferred Qualifications
Experience with distributed computing frameworks such as Apache Spark or Hadoop.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Knowledge of big data technologies and tools such as SQL, NoSQL, and data lakes.
Experience with containerization and orchestration tools such as Docker and Kubernetes.
Contributions to open-source projects or publications in machine learning conferences/journals.
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Languages
- English
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