Clera
Machine Learning Engineer
About this role
Mid-level Machine Learning Engineer role at an AI recruitment startup, responsible for designing, deploying, and maintaining production ML models across the full lifecycle. You'll partner with product and engineering teams to translate business needs into scalable ML solutions while optimizing performance in real-world environments.
What you'll do
- Design, train, and evaluate machine learning models for production use cases
- Build and maintain end-to-end ML pipelines including data preprocessing, model serving, and monitoring
- Translate business requirements into technical ML solutions in collaboration with product and engineering
- Debug, optimize, and iterate on model performance based on production feedback
- Develop clean, maintainable code and contribute to ML infrastructure and tooling
- Participate in code reviews and mentor team members on ML best practices
What they're looking for
- Python for machine learning development
- TensorFlow, PyTorch, or scikit-learn
- End-to-end ML pipeline implementation
- Feature engineering and model evaluation
- MLOps tools and cloud platforms (AWS SageMaker, GCP Vertex AI, Kubernetes, Docker)
- A/B testing and experimentation frameworks
- Production model monitoring and debugging
- SQL and data manipulation
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Describe a production ML system you built end-to-end—what were the biggest challenges in moving it from development to deployment?
- How do you approach debugging and optimizing model performance when it underperforms in production versus your test environment?