Growth Machine Learning Engineer

Role overview

Growth Machine Learning Engineer at ClickUp. You’ll own the full lifecycle of production ML systems—working at the intersection of machine learning, data science, and MLOps to deliver reliable, low-latency models that power business decisions.

Responsibilities

• Model development & deployment: Deploy production-grade ML models with reliability, low latency, and scalability.
• MLOps & infrastructure: Build and maintain end-to-end ML pipelines, including automated training, evaluation, versioning, deployment, and monitoring.
• Feature engineering: Design, implement, and optimize feature pipelines (data quality and freshness).
• Model performance & monitoring: Create monitoring to track performance, detect drift, and trigger retraining.
• Data science enablement: Turn research prototypes into production systems; build tooling that accelerates experimentation.
• Cross-team collaboration: Bridge data science and software engineering for seamless integration into product/platform architectures.
• Performance optimization: Improve inference speed, pipeline efficiency, and system scalability.

Requirements

• 4+ years of experience in ML engineering, data engineering, or related roles; 2+ years building and deploying ML systems in production.
• Python proficiency.
• ML frameworks: TensorFlow, PyTorch, scikit-learn (at least one strongly; listed examples).
• MLOps tools/platforms: MLflow, SageMaker, Kubeflow, Vertex AI.
• SQL skills; experience with data warehouses and feature stores.
• Big data + streaming: Spark, Hadoop, and streaming frameworks.
• Cloud + containers: AWS, GCP, Azure; Docker, Kubernetes.
• CI/CD experience applied to ML workflows.
• Strong ML knowledge: algorithms, model evaluation, feature engineering, experiment tracking.
• Communication and stakeholder collaboration.

Nice-to-haves

• Bachelor’s or Master’s in CS, Machine Learning, Data Science, Engineering, or related field.