Machine Learning Engineer

ADT is hiring a Machine Learning Engineer for its Data & AI organization. The role designs, builds, deploys, and scales production machine learning models and AI systems across cloud architectures and edge devices. It also operationalizes AI algorithms, automates model training and monitoring, and collaborates with data science, data engineering, software engineering, and product teams.

Responsibilities:

• Production Deployment & Scaling: Operationalize and scale machine learning models into production systems via GCP Vertex AI, ensuring low-latency execution and high availability
• MLOps & Pipeline Automation: Design, implement, and maintain robust end-to-end ML pipelines for data preprocessing, continuous model training, and evaluation using Vertex AI Pipelines (Kubeflow/TFX) and GCP CI/CD toolchains
• Model Optimization: Optimize ML algorithms, large language models (LLMs), and deep learning models for computational efficiency using GCP infrastructure (GPUs/TPUs) and edge-computing constraints
• Monitoring & Drift Detection: Build tracking and observability systems to monitor production model performance, logging, and data/concept drift leveraging Vertex AI Model Monitoring and Google Cloud Storage solutions
• Infrastructure Collaboration: Partner with data engineering and platform teams to design, manage, and optimize scalable feature stores (Vertex AI Feature Store) and high-dimensional analytical data warehouses (BigQuery)
• Cross-Functional Integration: Collaborate with software engineering and product development teams to embed ML capabilities into customer-facing ADT applications and core services via microservices/APIs
• Code Quality & Best Practices: Champion rigorous software engineering principles within the data science organization, including modular code design, comprehensive testing (unit/integration), and technical documentation
• Continuous Innovation: Stay current with the rapidly evolving ML engineering landscape, adopting cutting-edge tools, framework upgrades, and hardware accelerations to keep ADT competitive

Requirements:

• Bachelor's degree (or equivalent experience) in Computer Science, Software Engineering, Data Science, Mathematics, or a highly quantitative field
• 3+ years of professional software engineering or machine learning engineering experience, with a proven track record of deploying models to production environments
• Advanced, production-grade proficiency in programming languages like Python, with strong object-oriented design skills
• Deep experience with core ML frameworks and libraries (e.g., PyTorch, TensorFlow, Scikit-Learn, XGBoost)
• Strong familiarity with Google Cloud Platform (GCP), specifically Vertex AI (Pipelines, Feature Store, Model Registry), BigQuery, and Google Cloud Storage (GCS)
• Solid experience constructing high-dimensional data pipelines using SQL, Spark on GCP, or Google Cloud Dataflow
• Strong understanding of Docker, Kubernetes (Google Kubernetes Engine - GKE), MLOps, CI/CD toolchains, and Bitbucket
• Versatility to manage multiple technical priorities, apply structure to complex engineering challenges, and thrive in a fast-paced environment
• Master's degree
• Google Cloud Certified Professional Machine Learning Engineer or Professional Cloud Architect