Growth Machine Learning Engineer, ClickUp

• Experience: 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
• Technical Skills:
• 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
• ML Knowledge: Strong understanding of machine learning algorithms, model evaluation techniques, feature engineering, and experiment tracking
• Soft Skills: Strong problem-solving abilities, excellent communication skills, and a collaborative mindset with the ability to work across technical and non-technical stakeholders

Desirable

• We are seeking a highly skilled and motivated ML Engineer to join our team
• This role sits at the intersection of machine learning, data science, and MLOps, requiring you to own the full lifecycle of ML systems — from feature engineering to model production deployment and monitoring
• You will collaborate closely with data scientists, analysts, and data engineering teams to build robust, scalable ML systems that drive impactful business decisions
• Model Development & Deployment: Deploy production-grade machine learning models, ensuring reliability, low latency, and scalability
• MLOps & Infrastructure: Build and maintain end-to-end ML pipelines, including automated training, evaluation, versioning, deployment, and monitoring workflows
• Feature Engineering: Partner with data scientists to design, implement, and optimize feature pipelines that feed into ML models, ensuring data quality and freshness
• Model Performance & Monitoring: Establish monitoring frameworks to track model performance, detect drift, and trigger retraining as needed
• Data Science Enablement: Work alongside data scientists to translate research prototypes into production-ready systems, and create tooling that accelerates experimentation
• Collaboration: Act as a bridge between data science and software engineering teams, ensuring seamless integration of ML models into broader product and platform architectures
• Performance Optimization: Continuously improve model inference speed, pipeline efficiency, and overall system scalability