Machine Learning Engineer

Summary:

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. 

The ideal candidate is a software-focused engineer with deep expertise in machine learning frameworks, data pipelines, and MLOps practices. You will work closely with Data Scientists, Data Engineers, and Product teams to operationalize AI algorithms, automate model retraining cycles, and ensure our intelligence systems are secure, scalable, and highly available.

Duties and 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. 

Minimum Qualifications:

• 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, 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. 

• Software Practices: Strong understanding of Docker, Kubernetes (Google Kubernetes Engine - GKE), MLOps, CI/CD toolchains, and Bitbucket.

• Ambiguity Management: Versatility to manage multiple technical priorities, apply structure to complex engineering challenges, and thrive in a fast-paced environment. 

Required Licensing or Certifications:

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

Communication Skills:

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

Environment Requirements: 

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

Travel:

• Occasionally, less than 25%