3+ years of professional machine learning or software engineering experience, specifically in production ML systems.
Experience in machine learning engineering, not just data science roles, with substantial post-education employment.
Completed degree with a solid understanding of model selection, evaluation metrics, feature engineering, and validation.
Proficient in Python with hands-on experience in TensorFlow, PyTorch, or scikit-learn.
Experience in implementing and maintaining production ML pipelines, including data preprocessing and monitoring.
Familiarity with MLOps tools and cloud platforms like AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
Experience with production A/B testing and optimizing models based on real-world results.
Background in a startup or fast-paced product environment, adept at prioritizing impact and navigating ambiguity.
Responsibilities
Design, train, and evaluate machine learning models for real-world applications.
Build end-to-end ML pipelines that include data preprocessing, model serving, and monitoring.
Collaborate with product and engineering teams to translate business needs into ML solutions.
Debug and enhance production model performance using monitoring and feedback.
Write maintainable code and contribute to ML infrastructure and tooling.
Participate in code reviews and share knowledge with team members.
Benefits
Opportunity to work on cutting-edge recruitment technology.
Collaborative environment with cross-functional teams.
Hands-on experience with the full ML lifecycle.
Potential for professional growth in a fast-paced startup culture.
On-site role in the vibrant city of San Francisco.
Full Job Description
About the Role
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. What You'll Do
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.
What We're Looking For
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.
Location
This is an on-site role based in San Francisco, California, United States.
