Key skills
PythonSQLAIMachine LearningMLPyTorchTensorFlowscikit-learnMLOpsMLflowKubeflowData EngineeringSparkHadoopAWSAzureGCPKubernetesDockerSageMakerVertex AICI/CDCommunication
About this role
ClickUp is a company focused on redefining the future of work through innovative software solutions. They are seeking a highly skilled ML Engineer to own the full lifecycle of machine learning systems, collaborating with data scientists and engineers to build robust, scalable ML systems that drive impactful business decisions.
Responsibilities:
- Deploy production-grade machine learning models, ensuring reliability, low latency, and scalability
- Build and maintain end-to-end ML pipelines, including automated training, evaluation, versioning, deployment, and monitoring workflows
- Partner with data scientists to design, implement, and optimize feature pipelines that feed into ML models, ensuring data quality and freshness
- Establish monitoring frameworks to track model performance, detect drift, and trigger retraining as needed
- Work alongside data scientists to translate research prototypes into production-ready systems, and create tooling that accelerates experimentation
- Act as a bridge between data science and software engineering teams, ensuring seamless integration of ML models into broader product and platform architectures
- Continuously improve model inference speed, pipeline efficiency, and overall system scalability
Requirements:
- 4+ years of experience in ML engineering, data engineering, or a related role, with at least 2 years focused on building and deploying machine learning systems in production
- Strong proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
- Hands-on experience with MLOps tools and platforms (e.g., MLflow, SageMaker, Kubeflow, Vertex AI)
- Solid SQL skills and experience with data warehouses and feature stores
- Experience with big data technologies (e.g., Spark, Hadoop) and streaming frameworks
- Expertise in cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes)
- Familiarity with CI/CD practices applied to ML workflows
- Strong understanding of machine learning algorithms, model evaluation techniques, feature engineering, and experiment tracking
- Strong problem-solving abilities, excellent communication skills, and a collaborative mindset with the ability to work across technical and non-technical stakeholders
- Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field