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.