Machine Learning Engineer

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The Machine Learning Engineer will be responsible for designing, building, and deploying production-grade machine learning models and AI systems within ADT's Data & AI organization. This role bridges the gap between data science research and software engineering, taking theoretical models and scaling them to run efficiently across ADT’s vast ecosystem—from high-throughput cloud architectures to edge devices and smart home IoT telemetry.

• 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 tool chains.
• 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 (Big Query).
• 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.

• Education:
  
  Bachelor’s degree (or equivalent experience) in Computer Science, Software Engineering, Data Science, Mathematics, or a highly quantitative field. (Master's degree preferred).
• Experience:
  
  3+ years of professional software engineering or machine learning engineering experience, with a proven track record of deploying models to production environments.
• Technical Proficiency:
• 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, Tensor Flow, Scikit-Learn, XGBoost).
• Strong familiarity with Google Cloud Platform (GCP), specifically Vertex AI (Pipelines, Feature Store, Model Registry), Big Query, and Google Cloud Storage (GCS).
• Solid experience constructing high-dimensional data pipelines using SQL, Spark on GCP, or Google Cloud Dataflow.
• Software Practices:
  Strong understanding of Docker, Kubernetes (Google Kubernetes Engine - GKE), MLOps, CI/CD tool chains, and Bitbucket.
• Ambiguity Management:
  Versatility to manage multiple technical priorities, apply structure to complex engineering challenges, and thrive in a fast-paced environment.

• Highly Preferred:
  Google Cloud Certified Professional Machine Learning Engineer or Professional Cloud Architect

• Writing, Talking/Hearing on the phone (Continually=67-100% of workday)

• Remote/Home office (Continually=67-100% of the workday)

• Occasionally, less than 25%

In the event a position is commission-based, compensation is solely…