Why This Role Stands Out
This role offers an exciting opportunity to build and deploy cutting-edge AI solutions within a dynamic recruiting platform, fostering significant career growth. You'll thrive here if you possess strong ML fundamentals and enjoy collaborating across teams to bring innovative models to life. Apply now to contribute to impactful projects and advance your skills in a reputable tech company.
Quick Overview
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.