About the Role
As a Machine Learning Engineer, you'll build and deploy production machine learning systems for an AI-powered recruiting platform. You'll work across the ML lifecycle, partnering with product and engineering to turn business needs into reliable models and features.
What You'll Do
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Design, train, and evaluate machine learning models for production use cases.
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Build end-to-end ML pipelines, from data preprocessing through model serving and monitoring.
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Collaborate with product and engineering teams to develop ML solutions for business needs.
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Monitor, debug, and optimize production model performance using real-world feedback.
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Write maintainable code and contribute to ML infrastructure and tooling.
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Participate in code reviews and share knowledge with teammates.
What We're Looking For
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At least 3 years of professional experience in machine learning or software engineering, including hands-on production ML work and employment after graduation.
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A completed degree and experience in a substantive ML role at a product company or startup.
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Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation.
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Proficiency in Python and practical experience with TensorFlow, PyTorch, or scikit-learn.
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Experience building and maintaining production ML pipelines, including preprocessing, serving, and monitoring.
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Familiarity with MLOps and cloud tools such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
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Experience with production A/B testing or experimentation, and comfort working in fast-moving environments.
Compensation & Benefits
Salary starts at $140,000 USD annually. Equity and visa sponsorship details were not provided.
Location
On-site in San Francisco, California, United States.
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