C
On-siteFull-timeMid levelEngineering
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$212K–$398K/yr
Estimate — the employer hasn't published pay.
Based on 15 similar mid-level Machine Learning Engineer jobs in San Francisco that show pay. The range is the middle half of their posted pay (25th to 75th percentile), not an offer — ask the employer for the real range.
Data Science & ML salaries →Build and deploy machine learning systems that power a recruitment technology product. You will contribute across the full ML lifecycle, from defining problems and evaluating models to deployment and production monitoring. The role works closely with product, engineering, and domain experts to deliver useful, reliable ML capabilities.
Design, train, and evaluate machine learning models for production use cases.
Build end-to-end ML pipelines covering data preprocessing, model serving, and monitoring.
Work with product and engineering partners to turn business requirements into ML solutions.
Debug and improve production model performance using monitoring and real-world feedback.
Write maintainable code and contribute to ML infrastructure and tooling.
Participate in code reviews and share knowledge with teammates.
At least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.
Professional machine learning engineering experience, not solely data science work, and substantive employment after completing studies.
A completed degree and demonstrated understanding of model selection, evaluation metrics, feature engineering, and validation.
Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
Experience implementing and maintaining production ML pipelines, including preprocessing, serving, and monitoring.
Familiarity with MLOps tools and cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
Experience with production A/B testing or experimentation frameworks, and optimizing models based on real-world results.
Experience in a startup or fast-moving product environment, with the ability to prioritize impact and work through ambiguity.
This is an on-site role based in San Francisco, California, United States.