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7 results for Qa Automation Engineer in Cerritos, CA

QA Analyst L2

Burbank, CA
onsite
Temporary / Contract
52 - 55 USD / Hourly
We are looking for a Level 2 QA Analyst to support privacy and website compliance initiatives in Southern California. This is a Long-term Contract position focused on ensuring applications, workflows, and data-handling practices align with regulatory expectations and internal quality standards. The role works closely with business, legal, compliance, and technology stakeholders to assess requirements, coordinate testing efforts, and confirm that privacy-focused solutions are delivered accurately and reliably. Responsibilities:• Build and carry out test strategies, test plans, and detailed test cases to evaluate privacy-related business and system requirements.• Verify that consent management processes, data subject request workflows, and handling of sensitive information function as intended and meet compliance expectations.• Perform functional, integration, and user acceptance testing for website and privacy compliance initiatives, documenting outcomes clearly.• Record, prioritize, and monitor defects through resolution while partnering with cross-functional teams to address root causes and retesting needs.• Produce QA artifacts such as traceability matrices, validation reports, test evidence, and defect summaries to support compliance reviews and audit readiness.• Maintain end-to-end traceability between requirements, test scenarios, execution results, and final validation status.• Review business and regulatory requirements to identify gaps, improve testability, and confirm complete test coverage.• Communicate testing progress, quality risks, compliance concerns, and critical issues to stakeholders, including recommendations for mitigation.• Assess opportunities to strengthen QA processes, reporting practices, and repeatable automation for compliance-focused testing activities.
2026-06-24T00:00:00Z

QA Analyst L2

Torrance, CA
onsite
Temporary / Contract
36 - 40 USD / Hourly
We are looking for a QA Analyst to support the testing of connected vehicle platforms, digital products, and mobile IoT applications. This role will be responsible for validating customer-facing applications and services across vehicle platforms, ensuring quality throughout the software development lifecycle for both new product development and in-market support initiatives.The ideal candidate will have strong experience in software testing, system analysis, mobile application testing, and defect management within Agile environments. This position requires collaboration with cross-functional teams to identify issues, validate solutions, and ensure seamless performance of connected vehicle technologies and digital experiences. Key Responsibilities:· Test connected vehicle platforms, digital products, mobile applications, and IoT services across vehicle programs and releases.· Execute functional, integration, regression, and end-to-end testing activities for web, mobile, and embedded systems.· Analyze system requirements, business requirements, and data flows to develop effective test strategies and test cases.· Identify, document, track, and validate defects using Jira and related defect management tools.· Support software releases within CI/CD environments and validate deployments across multiple testing stages.· Perform mobile application testing for both iOS and Android platforms.· Collaborate with development, product, engineering, and business stakeholders across multiple teams and regions.· Support API and database validation activities to ensure data accuracy and system reliability.· Participate in Agile ceremonies and contribute to continuous quality improvement initiatives.
2026-07-07T00:00:00Z

Machine Learning Engineer

Los Angeles, CA
onsite
Permanent / Full Time
180000 - 210000 USD / Yearly
We are looking for a Machine Learning Engineer to build and support production-ready AI systems in Los Angeles, California. This position focuses on creating reliable machine learning infrastructure, enabling scalable model operations, and advancing generative AI solutions that improve access to business-critical information. The ideal candidate will combine strong platform engineering skills with hands-on experience deploying models, automating workflows, and maintaining high standards for quality, governance, and performance. Responsibilities: • Build and maintain scalable machine learning infrastructure in Databricks, including experiment tracking, model management, and serving capabilities for production use. • Create and improve MLOps frameworks and automated deployment pipelines that support model release, monitoring, and lifecycle management. • Establish disciplined processes for model version control, retraining, and artifact governance using tools such as Unity Catalog. • Develop and administer a feature store strategy that keeps training and inference data consistent across machine learning workflows. • Design retrieval-augmented generation solutions that allow internal teams to search and interact with documents such as fund materials, investor communications, and research content. • Implement and manage vector search platforms to support semantic retrieval across large collections of enterprise documents. • Customize and fine-tune large language models using proprietary datasets while protecting data privacy and meeting compliance expectations. • Build document ingestion and transformation pipelines that handle parsing, segmentation, and embedding creation for generative AI applications. • Introduce prompt design standards, evaluation methods, and application safeguards to improve response quality, reduce hallucinations, and provide source-backed outputs. • Automate training, testing, orchestration, and deployment workflows through CI/CD pipelines and tools such as Databricks Workflows, GitHub Actions, Azure DevOps, Airflow, or Prefect.
2026-06-11T00:00:00Z

Machine Learning Engineer

Los Angeles, CA
onsite
Permanent / Full Time
200000 - 260000 USD / Yearly
RESPONSIBILITIES: ML Model Deployment & Platform Management • Lead the design, implementation, and ongoing maintenance of scalable ML infrastructure on Databricks, including ML flow for experiment tracking, model registry, and model serving endpoints. • Oversee the development of the ML Ops platform and automated pipelines for deploying, monitoring, and maintaining models within production environments. • Implement robust solutions for model versioning, systematic retraining, and comprehensive artifact management using Databricks Unity Catalog for ML governance. • Design and manage Databricks Feature Store for consistent feature engineering across training and inference pipelines. Generative AI & LLM Operations • Architect and implement Retrieval-Augmented Generation (RAG) systems for document Q&A, enabling business teams to query fund documents, investor letters, and market research. • Design, deploy, and manage vector database solutions (Databricks Vector Search, Pinecone, or similar) for semantic search and retrieval across enterprise documents. • Lead LLM fine-tuning and customization initiatives, training models like Claude or open-source alternatives with CIM proprietary data while ensuring data privacy and compliance. • Develop and optimize document processing pipelines including PDF parsing, chunking strategies, and embedding generation for RAG applications. • Implement prompt engineering best practices and LLM evaluation frameworks to ensure output quality, relevance, and factual accuracy. • Build guardrails and safety measures for GenAI applications, including hallucination detection, output validation, and source attribution. Automation & CI/CD Pipelines • Design and implement extensive automation across the ML workflow, covering model training, testing, validation, and deployment using Databricks Workflows and Asset Bundles. • Set up robust CI/CD pipelines for both traditional ML models and GenAI applications, leveraging GitHub Actions, Azure DevOps, or similar tools. • Automate complex data and model workflows utilizing orchestration tools such as Airflow, Prefect, or Databricks Workflows.
2026-06-11T00:00:00Z

DevOps Engineer

Rosemead, CA
onsite
Permanent / Full Time
175000 - 200000 USD / Yearly
Job TitleManager, DevOps Automation Company OverviewA well-established organization in the consumer services and technology sector, based in San Gabriel Valley, California, is focused on delivering scalable, technology-driven solutions to support enterprise operations. The company is committed to operational excellence, innovation, and building secure, reliable systems that enable business growth across a distributed environment. With a strong emphasis on modernization, the organization is investing in automation and cloud technologies to enhance efficiency and performance. Role SummaryThe Manager, DevOps Automation will lead the strategy, implementation, and evolution of infrastructure automation and Infrastructure as Code (IaC) capabilities across enterprise platforms in San Gabriel Valley, California. This role combines hands-on technical leadership with team management, driving the adoption of scalable, secure, and efficient automation practices across cloud and on-premises environments. The position plays a critical role in reducing manual processes, improving system reliability, and advancing overall technology maturity. Key Responsibilities<ul><li>Lead the design, implementation, and governance of DevOps automation and IaC solutions across hybrid infrastructure environments</li><li>Manage and mentor a team of DevOps engineers, fostering growth, performance, and technical excellence</li><li>Serve as the subject matter expert for automation tools, including orchestration platforms and configuration management systems</li><li>Develop and standardize automation frameworks for provisioning, patching, compliance, and lifecycle management</li><li>Partner with infrastructure, cloud, and security teams to integrate automation into CI/CD pipelines and operational workflows</li><li>Oversee automation and operational excellence for identity and productivity platforms, including directory services and collaboration tools</li><li>Establish and track key performance indicators related to automation coverage, system reliability, and operational efficiency</li><li>Drive continuous improvement by reducing manual intervention and enhancing consistency and auditability</li><li>Evaluate emerging technologies and tools to advance automation capabilities and standardization</li><li>Create and maintain documentation, standards, and reference architectures to support enterprise-wide adoption</li></ul>Compensation & Benefits<ul><li>$160,000-$175,000 + discretionary bonus + stock</li><li>Performance-based bonus opportunities</li><li>Comprehensive health, dental, and vision insurance with significant employer contribution</li><li>Retirement savings plan with company match</li><li>Stock or equity participation opportunities</li><li>Employee discounts and wellness-focused perks</li><li>Paid time off and flexible scheduling options</li></ul>Additional Details<ul><li>Hybrid work model with regular in-office collaboration</li><li>Leadership role with direct reports and significant organizational impact</li><li>Occasional travel and flexibility for after-hours support as needed</li><li>Opportunity to shape and scale enterprise automation strategy in a dynamic environment</li></ul>
2026-07-15T00:00:00Z

Security Data Engineer

Costa Mesa, CA
remote
Temporary to Hire
60 - 65 USD / Hourly
We are looking for a Security Data Engineer to join our team in Costa Mesa, California in a contract capacity with the potential for a permanent role. This position is focused on advancing a well-established data lake environment by strengthening the ingestion layer, expanding connectivity to additional systems, and ensuring reliable movement of security-relevant data. The person in this role will take ownership of pipeline operations, partner with internal system stakeholders to enable access, and help keep incoming data accurate, timely, and usable for downstream needs. Responsibilities: • Take ownership of the data ingestion layer for an existing data lake, ensuring steady performance and dependable delivery of incoming datasets. • Reduce a defined backlog of pipeline work during the first 90 days by building and activating prioritized ingestion workflows. • Assume responsibility for pipeline components transitioned from the lead engineer and continue their operational support without disrupting established foundations. • Support and improve current ETL processes by monitoring health, resolving failures, and extending coverage where needed. • Integrate new source systems, including platforms such as Rippling and Workday, into the broader data ecosystem. • Coordinate with internal application and system owners to identify data sources, secure appropriate access, and obtain logs or source records required for ingestion. • Apply data cleaning and analysis practices to improve data quality and maintain consistency across inbound datasets. • Contribute to infrastructure and deployment workflows using AWS, AWS CDK, Infrastructure as Code, and CI/CD practices to support scalable pipeline operations.
2026-06-29T00:00:00Z

Data & ML Platform Engineer Manager

Inglewood, CA
onsite
Permanent / Full Time
180000 - 200000 USD / Yearly
Job TitleManager, Data & Machine Learning Platform Engineer Company OverviewA leading organization in the sports and entertainment industry is redefining the live event and fan engagement experience through data and AI. By leveraging cutting-edge technology and advanced analytics, the organization is building innovative platforms that deliver personalized, seamless experiences for millions of fans. Role SummaryThis Los Angeles-based role is a hands-on opportunity for a Manager, Data & Machine Learning Platform Engineer to design and build end-to-end data and ML systems that power intelligent products across fan engagement, marketing, and operations. Acting as an individual contributor, you will own the full lifecycle of data and machine learning platforms—from data ingestion to real-time model deployment—enabling scalable, production-ready AI solutions. Key Responsibilities<ul><li>Design, build, and maintain scalable data pipelines and ML platform infrastructure for analytics and AI use cases</li><li>Own the end-to-end lifecycle of data and ML systems, including ingestion, transformation, feature engineering, deployment, and monitoring</li><li>Develop and optimize data models and schemas to support both batch and real-time workflows</li><li>Build and manage feature pipelines and enable low-latency access for real-time decisioning systems</li><li>Deploy machine learning models into production via APIs and real-time inference services</li><li>Implement CI/CD pipelines for machine learning workflows, including testing, versioning, and automated deployment</li><li>Establish monitoring systems to track data quality, model performance, and system reliability</li><li>Enable experimentation frameworks such as A/B testing to support data-driven product iteration</li><li>Collaborate cross-functionally with data scientists, product teams, and business stakeholders to deliver impactful AI solutions</li><li>Drive architectural decisions and promote best practices in data engineering and MLOps</li></ul>Compensation & Benefits<ul><li>$180,000 – $200,000</li><li>Comprehensive benefits package including medical, dental, and vision coverage</li><li>401(k) with company contribution</li><li>Annual wellbeing allowance</li><li>Flexible paid time off and parental leave</li><li>Company-paid life and disability insurance</li><li>Mental health and wellness support programs</li><li>Flexible spending accounts and family planning assistance</li></ul>Additional Details<ul><li>Work model: Hybrid (4 days onsite per week)</li><li>Core working hours with flexibility</li><li>Individual contributor role (no direct reports)</li><li>High-impact role contributing to enterprise-wide AI initiatives</li></ul>
2026-06-12T00:00:00Z