Role Overview:
We are seeking hands-on Machine Learning Engineers for an urgent staff augmentation engagement to support, fix, and optimize the current Bumble 1.0 platform.
Operating under the direct guidance of the Staff ML Architect, you will focus heavily on daily MLOps execution, pipeline maintenance, and ensuring models perform reliably in a high-traffic environment.
Key Responsibilities
● Model Lifecycle Management:
Design, deploy, and monitor machine learning models within high-traffic environments, ensuring maximum reliability, performance, and scalability.
● Pipeline Execution:
Maintain, troubleshoot, and optimize end-to-end ML pipelines.
Integrate tools like Spark and Airflow to streamline the flow from raw data ingestion through to offline and online model evaluation.
● Daily MLOps:
Execute daily model training and inference tasks. Build and manage automated containerized deployments to ensure smooth, continuous support for the Bumble 1.0 platform.
Required Qualifications
● Solid, hands-on experience with the GCP ecosystem, particularly Vertex AI components (Workbench, Pipelines, Model Registry).
● Proficiency in modern ML frameworks (e.g., PyTorch) and containerization tools (Docker) for automated builds.
● Practical experience managing data processing flows using Apache Spark and Airflow.
● Familiarity with real-time model serving and infrastructure (e.g., Triton Inference Server,
Terraform) is highly preferred.
● Strong collaborative mindset with the ability to execute technical tasks reliably under the guidance of a senior architect.