Machine Learning Engineer (Mid-Level)
clera · San Francisco ·
- Work mode
- Onsite
- Seniority
- Middle
- Employment
- Full time
- Category
- ML ai
- Experience
- 3+ years
clera · San Francisco ·
Build and deploy production machine learning systems for clera's AI recruiting product, owning models end-to-end from problem definition and training through pipelines, serving, and production monitoring. Core stack is Python with TensorFlow/PyTorch/scikit-learn plus MLOps tooling on cloud platforms.
Join a pre-seed AI recruiting technology startup as a Machine Learning Engineer. You will build and deploy machine learning systems that power the core product, owning work from problem definition through production monitoring. The role works closely with product, engineering, and domain experts to deliver useful, reliable models.
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.
Partner with product and engineering teams to turn business needs into ML solutions.
Debug and improve model performance using production monitoring and real-world feedback.
Write maintainable code and contribute to ML infrastructure, tooling, and code reviews.
Share knowledge with teammates and support a culture of rapid iteration.
At least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.
A completed degree and strong machine learning fundamentals, including model selection, evaluation metrics, feature engineering, and validation.
Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
Experience implementing production ML pipelines with data preprocessing, model serving, and monitoring.
Familiarity with MLOps tools and cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
Experience maintaining and optimizing deployed systems, and using A/B testing or other production experimentation methods.
Comfort working in a fast-moving product environment with ambiguity and changing priorities.
This is an on-site role based in San Francisco, United States.