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 • Design, train, and evaluate machine learning models for production use cases. • Build end-to-end ML pipelines, from data preprocessing through model serving and monitoring. • Work with product and engineering partners to translate business requirements into ML solutions. • Debug and improve models in production using monitoring data and real-world feedback. • Write maintainable code and contribute to ML infrastructure and tooling. • Participate in code reviews and share knowledge with teammates. What We're Looking For • A degree and at least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems. • Strong understanding of model selection, evaluation metrics, feature engineering, and validation techniques. • Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn. • Experience implementing production pipelines for data preprocessing, model serving, and monitoring. • Experience maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker. • Experience with production A/B testing or experimentation frameworks. • 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.