About the Role
Join a small, fast-moving team building AI-powered talent and recruiting technology. You will design, deploy, and maintain machine learning systems across the full lifecycle, helping turn product needs into reliable production capabilities.
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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Work with product and engineering partners to translate business requirements into ML solutions.
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Debug and improve models in production using monitoring data and 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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A degree and at least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.
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Strong understanding of model selection, evaluation metrics, feature engineering, and validation techniques.
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Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
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Experience implementing production pipelines for data preprocessing, model serving, and monitoring.
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Experience maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
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Experience with production A/B testing or experimentation frameworks.
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Background working in fast-moving product environments and iterating on ML systems based on production feedback.
Compensation & Benefits
Base salary: $140,000 USD annually. Visa sponsorship information was not provided.
Location
On-site in San Francisco, California, United States.
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