Machine learning engineer • san jose ca
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The average salary range is between $ 149,072 and $ 216,347 year , with the average salary hovering around $ 173,653 year .
The average salary range is between $ 124,693 and $ 200,000 year , with the average salary hovering around $ 154,997 year .
Description
We are seeking a Senior ML Engineer to join our team of dedicated machine learning and software engineers. This role involves timely engineering, system development, ML model assessment, constructing data pipelines, composing prototypes, evaluation, operationalization.
Looking for individuals skilled in classical and deep learning, able to build and implement ML solutions for production environments. If you thrive on delivering innovative customer features, adopting new technologies, and contributing to high-visibility projects, this role may be a good match. The atmosphere is dynamic, fast-paced, creative, collaborative, and data-centric.
What you’ll do
Contribute to the backend services supporting ML and Generative-AI functionalities.
Build, develop, implement, and evaluate machine learning models using classical and deep learning approaches, as well as GenAI.
Collaborate with cross-functional teams to integrate ML solutions into existing products and services.
Analyze and improve efficiency, accuracy, scalability, and stability of AI-enabled workflows.
Stay current with the latest advancements in ML and AI technologies.
Guide and support junior team members to aid in the overall development of the ML team.
What you need to succeed
Required Qualifications:
Completed a degree in Computer Science, Machine Learning, or a related field.
5+ years of experience in machine learning engineering roles.
Strong software engineering skills, including version control, code review, and CI/CD practices.
Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, scikit-learn, Sagemaker.
Experience in developing and deploying ML models in production environments.
Familiarity with cloud platforms (, AWS, GCP, Azure) for ML deployment.
Strong understanding of data structures, algorithms, and system development.
Strong leadership and team-building skills.
Excellent communication and presentation skills.
Familiarity with Generative AI technologies and applications is advantageous.
Familiarity with Retrieval-Augmented Generation (RAG) and other contemporary NLP methods is advantageous.
Having experience with MLOps practices and tools is advantageous.
Having a background in Recommendation Systems is advantageous.
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