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Machine Learning Engineer

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Machine Learning Engineer in USA

  • San Francisco, CA

Responsibilities

  • Design, train, and evaluate machine learning models for production use cases.
  • Build end-to-end ML pipelines for data preprocessing, model serving, and monitoring.
  • Translate business requirements into machine learning solutions with product and engineering teams.
  • Debug and optimize model performance in production using real-world feedback.
  • Write maintainable code and contribute to ML infrastructure and tooling.
  • Participate in code reviews and share knowledge across the team.

Requirements

  • 3–7 years of professional experience in machine learning or software engineering with hands-on applied ML work in production systems.
  • Experience working in a startup or similarly fast-paced, resource-constrained environment.
  • Strong fundamentals in model selection, evaluation, feature engineering, and validation.
  • Proficiency in Python and common ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
  • Experience building, deploying, and maintaining ML systems at scale rather than only academic or prototype systems.
  • Experience with data pipelines, feature engineering, or model evaluation in production contexts.
  • Familiarity with cloud ML platforms or MLOps tooling such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker is preferred.
  • Experience with A/B testing, experimentation frameworks, or production model monitoring is preferred.
  • Ability to prioritize impact and work comfortably with ambiguity in a dynamic environment.

Benefits

  • Based in San Francisco, California; specific work arrangement should be confirmed directly with the team.

Clera

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