About the RoleJoin 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 & BenefitsBase salary: $140,000 USD annually. Visa sponsorship information was not provided.
LocationOn-site in San Francisco, California, United States.
