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Machine Learning Engineer (Mid-Level)

CleraSan Francisco🇺🇸United StatesPosted Oct 15, 2026

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
On Site
Location
San Francisco, United States
Posted
4 hours ago
DockerGCPAWSMLOpsMachine LearningScikit-learnA/B TestingKubernetesPyTorchPythonRecruitingTensorFlow

Job Description

About the Role

As a Machine Learning Engineer, you'll build and deploy production machine learning systems for an AI-powered recruiting platform. You'll work across the ML lifecycle, partnering with product and engineering to turn business needs into reliable models and features.

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.

  • Collaborate with product and engineering teams to develop ML solutions for business needs.

  • Monitor, debug, and optimize production model performance using 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

  • At least 3 years of professional experience in machine learning or software engineering, including hands-on production ML work and employment after graduation.

  • A completed degree and experience in a substantive ML role at a product company or startup.

  • Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation.

  • Proficiency in Python and practical experience with TensorFlow, PyTorch, or scikit-learn.

  • Experience building and maintaining production ML pipelines, including preprocessing, serving, and monitoring.

  • Familiarity with MLOps and cloud tools such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.

  • Experience with production A/B testing or experimentation, and comfort working in fast-moving environments.

Compensation & Benefits

Salary starts at $140,000 USD annually. Equity and visa sponsorship details were not provided.

Location

On-site in San Francisco, California, United States.

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