<p><strong>Machine Learning Engineer</strong></p><p><br></p><p><strong>Company Overview</strong></p><p>Based in Los Angeles, California, the company specializes in transforming complex, multi-source data into actionable insights through machine learning, knowledge graph technologies, and advanced analytics. This is an opportunity to work on mission-critical applications in a highly collaborative environment focused on innovation, scalability, and operational excellence.</p><p><br></p><p><strong>Role Summary</strong></p><p>The Machine Learning Engineer will play a critical role in designing, training, deploying, and optimizing machine learning models that operate on large-scale temporal, geospatial, relational, and unstructured datasets. </p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Design, train, evaluate, deploy, and optimize machine learning models across multiple production use cases.</li><li>Build predictive solutions for anomaly detection, forecasting, entity resolution, relationship prediction, risk assessment, and operational decision support.</li><li>Partner with data engineering teams to develop high-quality training datasets from structured, unstructured, temporal, relational, and geospatial data sources.</li><li>Design model architectures and select appropriate algorithms based on business objectives, data characteristics, and operational requirements.</li><li>Develop and maintain machine learning pipelines spanning data preparation, feature engineering, training, evaluation, deployment, and monitoring.</li><li>Build scalable solutions that leverage graph-based and knowledge graph-driven data architectures.</li><li>Establish rigorous evaluation frameworks, baselines, performance metrics, and validation methodologies.</li><li>Design experiments that mitigate data leakage, model drift, bias, and changing data distributions.</li><li>Implement monitoring, observability, alerting, retraining, rollback, and model governance processes.</li><li>Maintain reproducible datasets, model artifacts, evaluation results, and deployment workflows.</li><li>Collaborate with distributed engineering teams to deliver reliable and scalable machine learning capabilities.</li><li>Improve model calibration, confidence scoring, uncertainty estimation, and explainability.</li><li>Contribute to technical architecture, machine learning standards, and long-term platform strategy.</li></ul>
<p>We are looking for an expierenced Automation Integration Engineer to help strengthen and advance automated distribution operations in Riverside, California. This position combines hands-on engineering support with system integration work to keep warehouse technology, material handling equipment, and software platforms performing reliably in a high-volume retail environment. The role partners closely with operations, IT, engineering, and external vendors to resolve technical issues, improve throughput, and support new automation initiatives.</p><p><br></p><p>Responsibilities:</p><p>• Coordinate the connection and performance of warehouse software, automation controls, and material handling equipment to ensure smooth operation across distribution processes.</p><p>• Convert operational needs into detailed technical documentation, including functional requirements, change requests, and integration specifications.</p><p>• Build and manage testing strategies for system updates and automation enhancements, then verify results before release into production.</p><p>• Investigate interface failures, process interruptions, and unexpected system behavior by tracing data flow and equipment interaction across connected platforms.</p><p>• Provide daily engineering support for live automated operations, addressing equipment and system issues that affect flow, accuracy, or productivity.</p><p>• Lead root cause analysis for operational disruptions and follow issues through corrective action until stable performance is restored.</p><p>• Identify and implement improvements that increase throughput, strengthen reliability, and streamline warehouse processes.</p><p>• Assist with implementation projects, commissioning efforts, and go-live activities by coordinating technical tasks across internal teams and third-party partners.</p><p>• Maintain clear technical documentation such as standard operating procedures, workflow diagrams, interface records, and system support materials.</p><p>• Use operational metrics and system data to evaluate performance, support troubleshooting efforts, and contribute to reporting on key distribution indicators.</p>