Machine Learning Engineer (Mid-Level)
At a glance
Mid-level Machine Learning Engineering role at Clera. San Francisco · full-time · $140,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Train and assess models for real production needs, then keep them running after launch
- Build ML workflows that take data through preparation, serving and ongoing monitoring
- Turn product and engineering requests into machine learning solutions with those teams
- Use monitoring results and user feedback to troubleshoot and refine deployed models
- Write maintainable code, improve ML infrastructure, and review code with teammates
What you bring
- A degree and at least three years in machine learning or software engineering, including production ML delivery
- Strong grounding in choosing models, engineering features, checking performance and validating results
- Python proficiency plus practical use of TensorFlow, PyTorch or another listed library
- Experience building and supporting production ML pipelines, including data preparation, serving and monitoring
- Experience scaling ML systems with AWS SageMaker, GCP Vertex AI or another listed platform
Who this fits
This mid-level role suits someone who has built and deployed production ML systems and improved them using feedback from live use. The job is on-site in San Francisco.
From the employer
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.