Machine Learning Engineer

Machine Learning Engineer

Company Overview 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.

Role Summary 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.

Key Responsibilities

• Design, train, evaluate, deploy, and optimize machine learning models across multiple production use cases.
• Build predictive solutions for anomaly detection, forecasting, entity resolution, relationship prediction, risk assessment, and operational decision support.
• Partner with data engineering teams to develop high-quality training datasets from structured, unstructured, temporal, relational, and geospatial data sources.
• Design model architectures and select appropriate algorithms based on business objectives, data characteristics, and operational requirements.
• Develop and maintain machine learning pipelines spanning data preparation, feature engineering, training, evaluation, deployment, and monitoring.
• Build scalable solutions that leverage graph-based and knowledge graph-driven data architectures.
• Establish rigorous evaluation frameworks, baselines, performance metrics, and validation methodologies.
• Design experiments that mitigate data leakage, model drift, bias, and changing data distributions.
• Implement monitoring, observability, alerting, retraining, rollback, and model governance processes.
• Maintain reproducible datasets, model artifacts, evaluation results, and deployment workflows.
• Collaborate with distributed engineering teams to deliver reliable and scalable machine learning capabilities.
• Improve model calibration, confidence scoring, uncertainty estimation, and explainability.
• Contribute to technical architecture, machine learning standards, and long-term platform strategy.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, Data Science, or a related field, or equivalent practical experience.
• 5+ years of experience building, deploying, and operating production machine learning systems.
• Proven ability to independently own end-to-end machine learning development, deployment, monitoring, and optimization.
• Strong Python software engineering skills with experience developing production-grade applications.
• Demonstrated expertise developing and training machine learning models from raw data through production deployment.
• Experience designing datasets, feature engineering pipelines, and model training strategies.
• Hands-on experience with machine learning operations (MLOps), including deployment automation, monitoring, model drift detection, retraining, and lifecycle management.
• Broad experience across multiple machine learning domains, including computer vision, natural language processing (NLP), geospatial analytics, forecasting, anomaly detection, or entity resolution.
• Experience working with graph-based data models, relational data, or knowledge graph environments.
• Strong understanding of experimental design, model evaluation, calibration, uncertainty estimation, and error analysis.
• Experience working with complex, incomplete, evolving, or heterogeneous datasets.
• Excellent written and verbal communication skills.

Preferred Qualifications (Nice-to-Haves)

• Experience leading machine learning initiatives across multiple concurrent production models and use cases.
• Expertise in knowledge graph technologies, graph machine learning, link prediction, or entity matching.
• Experience with geospatial intelligence, trajectory analysis, location-based analytics, or time-series modeling.
• Experience with vector databases, graph databases, PostgreSQL, analytical lakehouse platforms, or large-scale data infrastructure.
• Experience serving machine learning models in cloud, on-premises, edge, or disconnected environments.
• Knowledge of model optimization techniques including quantization, distillation, and efficient inference.
• Familiarity with TypeScript or full-stack integration of machine learning capabilities into production applications.
• Experience mentoring engineers, establishing machine learning best practices, and defining technical standards.