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27 results for Systems Engineer in Inglewood, CA
- Glendale, CA
- onsite
- Temporary / Contract
- 72 - 78 USD / Hourly
- <p>We are looking for a Data Governance Engineer to support the implementation, administration, and continuous improvement of enterprise data governance capabilities across a modern cloud data platform. This role will work closely with data platform, security, and product teams to translate governance requirements into scalable technical solutions that improve data security, accessibility, quality, and compliance.</p><p><br></p><p><strong>Key Responsibilities:</strong></p><p>· Administer and configure Snowflake security and governance capabilities, including RBAC, dynamic masking, and object tagging.</p><p>· Implement, maintain, and troubleshoot access governance controls within Immuta and other governance platforms.</p><p>· Configure and monitor data quality checks, alerting mechanisms, and governance-related operational processes.</p><p>· Maintain, enrich, and optimize enterprise data catalog content to improve discoverability, classification, and metadata quality.</p><p>· Support the adoption and enhancement of AI-assisted data documentation and description generation tools.</p><p>· Partner with engineering teams to test, validate, and deploy ingest-time data tagging and metadata management solutions.</p><p>· Collaborate with program and product stakeholders to translate governance policies, security requirements, and compliance standards into technical implementations.</p><p>· Support governance operations, incident resolution, root cause analysis, and continuous process improvement initiatives.</p><p>· Participate in evaluations, testing, and implementation of new governance, catalog, quality, and security technologies.</p>
- 2026-10-29T00:00:00Z
- Los Angeles, CA
- onsite
- Permanent / Full Time
- 200000 - 300000 USD / Yearly
- <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>
- 2026-10-30T00:00:00Z