<p><strong>Job Title</strong></p><p>Manager, Data &amp; Machine Learning Platform Engineer</p><p><br></p><p><strong>Company Overview</strong></p><p>A 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.</p><p><br></p><p><strong>Role Summary</strong></p><p>This Los Angeles-based role is a hands-on opportunity for a Manager, Data &amp; 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.</p><p><br></p><p><strong>Key Responsibilities</strong></p><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><p><strong>Compensation &amp; Benefits</strong></p><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><p><strong>Additional Details</strong></p><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>

<p><strong>Required Qualifications</strong></p><ul><li>4+ years of experience in data engineering, machine learning engineering, or MLOps</li><li>Proven track record of building end-to-end data and ML systems in production environments</li><li>Strong proficiency in Python and SQL</li><li>Experience with cloud platforms (Azure, AWS, or GCP) and distributed systems</li><li>Expertise in building and maintaining data pipelines (batch and streaming)</li><li>Experience deploying ML models into production, including real-time inference systems</li><li>Hands-on experience with CI/CD pipelines for ML workflows and containerization tools (e.g., Docker, Kubernetes)</li><li>Strong background in designing scalable data models and working with large-scale datasets</li></ul><p><strong>Preferred Qualifications (Nice-to-Haves)</strong></p><ul><li>Experience with modern data platforms such as Databricks, Snowflake, or similar tools</li><li>Exposure to feature stores and real-time data processing systems</li><li>Experience with LLM infrastructure, embeddings, or vector search</li><li>Background in building experimentation or A/B testing platforms</li><li>Experience working in consumer-facing industries such as sports, entertainment, or digital platforms</li><li>Familiarity with personalization, recommendation systems, or forecasting models</li></ul>

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