Movement Search & Delivery

Job Description Senior Engineer, Machine Learning Compensation: $130,000-$140,000 + 6% Annual Bonus The Senior Engineer, Machine Learning will lead the development, testing, validation, and deployment of machine learning models that improve product performance, installation quality, outage detection, and equipment reliability. This role focuses on analyzing large-scale data generated by millions of deployed meters, sensors, radios, and connected devices to identify performance issues, detect anomalies, and drive actionable solutions. A critical requirement for this position is hands-on experience personally building and training machine learning models, along with implementing anomaly detection algorithms within those models. Candidates must have direct experience developing these models, not simply using existing models or supporting another team's development efforts. Explore the Depth of the Role Machine Learning Model Development Personally build, train, test, and deploy machine learning models, including predictive models, classifiers, and anomaly detection algorithms. Develop time-series data mining and anomaly detection solutions to identify unusual behavior, performance degradation, and potential equipment failures. Design and maintain end-to-end machine learning pipelines, from data preparation and model training through validation, deployment, and production monitoring. Monitor model performance, identify model drift, and implement improvements to maintain accuracy and reliability. Establish testing and validation methods to confirm model results against real-world data. Integrate model outputs into reporting tools and dashboards to support engineering and customer-facing teams. Technical Leadership & Collaboration Lead machine learning projects and investigations, defining technical requirements, scope, schedules, and deliverables. Establish model development standards and validation criteria. Mentor engineers and provide guidance on data preparation, model development, and deployment practices. Collaborate with software developers, data engineers, data scientists, and reliability and quality engineering teams. Communicate technical findings and recommendations to leadership and non-technical stakeholders. Data Infrastructure & Integration Develop and maintain data pipelines integrating internal and external sources, including sensor, carrier network, weather, and manufacturing test data. Work with AWS cloud infrastructure and machine learning platforms, including SageMaker, S3, EC2, ECS, and Glue. Utilize data technologies such as Elasticsearch, Amazon Redshift, and NoSQL databases. Support scalable data architecture and near-real-time analysis of large-scale time-series datasets. Qualifications: Education Bachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, Data Analytics, or a related field. Equivalent practical experience may be considered. Required Experience & Technical Skills 5+ years of experience in machine learning, data science, or applied analytics. Demonstrated experience personally building and training machine learning models. Direct experience implementing anomaly detection algorithms within machine learning models you have developed. This is mandatory. Strong proficiency in Python and SQL. Experience with predictive modeling, classification, statistical modeling, and time-series analysis. Experience working with large-scale sensor, IoT, endpoint, or other time-series data. Experience designing, validating, deploying, and monitoring machine learning models in production. Familiarity with GitHub, Jupyter Notebooks, and data visualization tools. Experience with cloud-based machine learning platforms, preferably AWS. Familiarity with Elasticsearch, Amazon Redshift, Grafana, and NoSQL data stores. Preferred Qualifications Experience with IoT devices, meters, sensors, radios, or connectivity equipment. Experience managing machine learning pipelines from development through production monitoring. Understanding of product reliability, device hardware, and failure analysis. Experience integrating third-party data sources into machine learning pipelines. Ability to independently lead technical projects and communicate complex findings. Familiarity with Lean Six Sigma or other continuous improvement methodologies. Competitive Total Rewards Competitive base salary of $130,000-$140,000 plus a 6% annual bonus. Eligible for annual pay increases. Comprehensive health, vision, and dental coverage. 15 days of paid time off plus 11 paid holidays. Retirement savings plan with a 25% employer match on contributions up to 7% of eligible compensation. Additional employer contribution of 5% of eligible compensation to a Defined Contribution account annually. Access to a certified financial planner at no cost. Employer-paid basic life insurance, short-term disability, and Employee Assistance Program. Tuition reimbursement up to $5,250. Voluntary life insurance, long-term disability, accident, and critical illness coverage. Health Savings Account (HSA) and Flexible Spending Account (FSA) options.