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. 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.

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.

Develop models utilizing computer vision, NLP, geospatial analytics, and predictive modeling techniques.

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.

Additional Details Fully onsite 5 days a week Highly collaborative environment with strong emphasis on machine learning, knowledge graphs, and data-driven decision support

Opportunity to influence technical direction, machine learning standards, and model lifecycle practices across multiple initiatives

Candidates must be authorized to work in the United States and satisfy applicable regulatory employment requirements