<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. This position requires an experienced engineer who can independently own the full machine learning lifecycle, from dataset development and model architecture selection to deployment, monitoring, and continuous improvement. The ideal candidate brings broad expertise across computer vision, natural language processing (NLP), geospatial analytics, MLOps, and large-scale production machine learning environments.</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>Develop models utilizing computer vision, NLP, geospatial analytics, and predictive modeling techniques.</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><strong>Additional Details</strong></p><ul><li>Fully onsite 5 days a week</li><li>Highly collaborative environment with strong emphasis on machine learning, knowledge graphs, and data-driven decision support</li><li>Opportunity to influence technical direction, machine learning standards, and model lifecycle practices across multiple initiatives</li><li>Candidates must be authorized to work in the United States and satisfy applicable regulatory employment requirements</li></ul>

<p><strong>Required Qualifications</strong></p><ul><li>Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, Data Science, or a related field, or equivalent practical experience.</li><li>5+ years of experience building, deploying, and operating production machine learning systems.</li><li>Proven ability to independently own end-to-end machine learning development, deployment, monitoring, and optimization.</li><li>Strong Python software engineering skills with experience developing production-grade applications.</li><li>Demonstrated expertise developing and training machine learning models from raw data through production deployment.</li><li>Experience designing datasets, feature engineering pipelines, and model training strategies.</li><li>Hands-on experience with machine learning operations (MLOps), including deployment automation, monitoring, model drift detection, retraining, and lifecycle management.</li><li>Broad experience across multiple machine learning domains, including computer vision, natural language processing (NLP), geospatial analytics, forecasting, anomaly detection, or entity resolution.</li><li>Experience working with graph-based data models, relational data, or knowledge graph environments.</li><li>Strong understanding of experimental design, model evaluation, calibration, uncertainty estimation, and error analysis.</li><li>Experience working with complex, incomplete, evolving, or heterogeneous datasets.</li><li>Excellent written and verbal communication skills.</li></ul><p><strong>Preferred Qualifications (Nice-to-Haves)</strong></p><ul><li>Experience leading machine learning initiatives across multiple concurrent production models and use cases.</li><li>Expertise in knowledge graph technologies, graph machine learning, link prediction, or entity matching.</li><li>Experience with geospatial intelligence, trajectory analysis, location-based analytics, or time-series modeling.</li><li>Background in Bayesian methods, probabilistic forecasting, survival analysis, or ensemble learning techniques.</li><li>Experience with vector databases, graph databases, PostgreSQL, analytical lakehouse platforms, or large-scale data infrastructure.</li><li>Experience serving machine learning models in cloud, on-premises, edge, or disconnected environments.</li><li>Knowledge of model optimization techniques including quantization, distillation, and efficient inference.</li><li>Experience supporting regulated, defense, public sector, intelligence, aerospace, or high-consequence operational environments.</li><li>Familiarity with TypeScript or full-stack integration of machine learning capabilities into production applications.</li><li>Experience mentoring engineers, establishing machine learning best practices, and defining technical standards.</li></ul><p><strong>Compensation &amp; Benefits</strong></p><ul><li>$200K-$300K + discretionary bonus</li><li>Performance-based bonus opportunities</li><li>Comprehensive medical, dental, and vision coverage</li><li>Retirement savings program</li><li>Generous paid time off and company holidays</li><li>Professional development and continuing education support</li><li>Opportunity to work with cutting-edge AI, machine learning, and analytics technologies</li></ul>

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