Machine Learning Engineer (Mid-Level)
Role Overview
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.
Key Skills for This Role
Full Job Posting
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.
Qualifications
- 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.
Responsibilities
- 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.
- 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.
- Base salary: $140,000 USD annually. Visa sponsorship information was not provided.
- On-site in San Francisco, California, United States.
About Clera
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