Senior Engineer, Machine Learning

Job Description
The Senior Engineer, Machine Learning will define the scope and tasks for machine learning initiatives. The successful candidate will possess strong knowledge of modeling techniques and system performance, and be able to define resources and time needed to estimate project efforts. This role focuses on the design, development, testing, validation, deployment, and ongoing tuning of machine learning pipelines and models that support product performance, installation quality, and outage detection. This includes fleet-wide health monitoring of the deployed population of millions of meters, sensors, radios, connectivity equipment, and other IoT devices in support of the Systems Integration team. The position also encompasses the data infrastructure, integration, and monitoring systems that keep those models reliable at scale. The Senior Engineer is expected to contribute quickly to the team's portfolio of algorithms, ML models, and AI tools, creating predictive models, classifiers, time series data mining, and anomaly detection algorithms, and will work closely with developers, data engineers, data scientists, and reliability, quality, and design engineering to identify and address product weaknesses. The Senior Engineer will represent the team on projects and investigations, report findings to leadership, and act as a mentor to other engineers.
ESSENTIAL JOB DUTIES:
Leadership & Scope-Setting

• Define the scope of machine learning projects and investigations; develop or provide input to schedules and budgets
• Define and control model design standards and validation criteria
• Mentor other engineers, on data ingestion, cleaning, and model development practices
• Provide oversight of model performance dashboards, ML-driven product support issues, and customer-impacting model behavior

ML Pipeline & Model Lifecycle

• Design, build, and maintain end-to-end machine learning pipelines, from data preparation through training, validation, deployment, and production monitoring
• Develop, tune, and maintain predictive models, classifiers, and AI tools, such as installation quality and outage detection models, to improve accuracy and reliability
• Develop time series data mining and anomaly detection algorithms to monitor the health and performance of millions of deployed meters, sensors, radios, and IoT devices
• Recommend corrective actions and trigger field responses, or hand off findings to design and reliability engineers
• Establish testing and validation frameworks to confirm model outputs against real-world data, including coordinating field or manual verification studies
• Monitor deployed models for drift, degraded performance, or unexpected outcomes, and lead remediation efforts
• Support integration of model outputs into customer-facing dashboards and reporting tools
• Identify opportunities to improve existing infrastructure, workflows, products, and investigations with machine learning models

Data Infrastructure & Integration

• Design and maintain data pipelines that integrate internal and external data sources, such as carrier network data, weather data, and manufacturing test data, into shared indices and datasets
• Interface with AWS cloud infrastructure for machine learning workloads, such as SageMaker, EC2, ECS, and Glue
• Utilize datastores such as Elasticsearch and Amazon Redshift, including indices and data structures supporting model training and inference
• Contribute to database and data architecture improvements as needed to support growing model and pipeline complexity

Cross-Functional Collaboration

• Partner with engineering, digital engineering, and analytics teams to align model outputs with product and business needs
• Work closely with developers, data engineers, data scientists, and reliability, quality, and design engineering to identify and address product weaknesses revealed by fleet and field data
• Share standardized machine learning libraries, tools, and best practices across teams to reduce duplicated effort and improve consistency
• Collaborate with customer-facing teams to validate model results and translate findings into actionable insights

Documentation & Process

• Initiate and manage tickets in the team's ticketing system (e.g., JIRA)
• Write, review, validate, and audit procedures related to model development, testing, and deployment

QUALIFICATIONS:
Education

• Bachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, or Data Analytics, or equivalent practical experience

Required Experience & Technical Skills

• 5+ years of related experience in machine learning, data science, or applied analytics; a Master's degree with related research may count toward a portion of this experience, depending on the topic and exposure to distributed sensor and device network data
• Strong foundation in engineering and statistical fundamentals
• Proficiency in Python and SQL; experience with NoSQL data stores, Elasticsearch, Amazon Redshift, Grafana, and Jupyter Notebooks; additional languages (e.g., C#) a plus
• Experience with version control tools and workflows (e.g., GitHub) for code and model management
• Experience with cloud-based data and machine learning platforms, preferably Amazon Web Services (e.g., S3, SageMaker, Redshift, EC2, ECS, Glue)
• Significant experience with statistics, machine learning, or other mathematical modeling and simulation techniques, including predictive modeling, classification, time series data mining, and anomaly detection
• Experience modeling performance and detecting anomalies in IoT device, sensor, or endpoint data at scale (e.g., fleets of meters, radios, or connectivity equipment)
• Experience with large-scale time series data sets and near-real-time analysis
• Experience designing, testing, validating, and deploying machine learning models in a production environment
• Experience with data visualization and business intelligence (BI) tools
• Ability to work with non-technical stakeholders to define expectations and success criteria for new models and algorithms, and to communicate results in clear, non-technical terms

Preferred Qualifications

• Ability to quickly develop working knowledge of metering, sensor, radio, and connectivity products
• Ability to independently solve problems and implement solutions
• Demonstrated judgment and decision-making within a defined level of authority
• Demonstrated ability to drive projects to completion
• Experience integrating external or third-party data sources (e.g., carrier network data, weather data, manufacturing/test data) into machine learning pipelines
• Experience managing machine learning pipelines end-to-end, from training through deployment and production monitoring
• Understanding of device hardware, reliability, or failure analysis sufficient to translate model findings into corrective-action recommendations (e.g., using field returns or test data)
• Familiarity with Lean Six Sigma or other continuous improvement methodologies