Senior Machine Learning Engineer focused on generative AI (mixed media, video, LLMs, agentic applications) and traditional ML for Disney's Ad Platforms. Responsibilities include developing, optimizing, and productionizing these technologies, creating ML datasets, evaluating quality, and mentoring junior engineers. The role emphasizes innovation in advertising domains like forecasting, pricing, and targeting.

Lead Machine Learning Engineer focused on generative AI (mixed media, video, audio, LLMs, agentic workflows) and traditional ML for ad platforms. Responsibilities include developing, optimizing, and productionizing these technologies, driving innovation, and building scalable ML systems. The role also involves mentoring junior engineers and working across various advertising domains like forecasting, pricing, and targeting.

Lead Machine Learning Engineer for Disney's Ad Platforms, focusing on applying and scaling AI/ML solutions for advertising domains like forecasting, targeting, and delivery. The role involves inventing, prototyping, deploying, and scaling ML systems, with a strong emphasis on LLMs, RAG, and evaluation methodologies. Requires significant experience in developing and deploying large-scale ML systems and mentoring junior engineers.

This role focuses on reengineering processes for AI-first environments, applying Lean Six Sigma methodologies, and driving measurable improvements in performance and customer outcomes. The Senior Analyst will architect AI-first process reengineering strategies, integrate cutting-edge technologies like Microsoft Copilot, develop AI agents to improve process and systems flow, and apply foundation models based on generative AI. The role also involves championing Lean Six Sigma, driving improvements in software/hardware lifecycle, and leading cross-functional initiatives.

Lead Software Engineer for Disney Entertainment and ESPN Product & Technology, focusing on building and operating content inspection pipelines. The role involves leading the development of systems that use computer vision and VLMs to analyze media assets, extracting temporal metadata for automated inspection, validation, and enrichment. This will improve content quality, operational efficiency, and media discovery across Disney's global content ecosystem. The engineer will influence architectural direction, collaborate with teams, and ensure system reliability and scalability.

Senior Engineering Manager to lead a software engineering team building and supporting ML-assisted content inspection pipelines within Disney's Content Platform Engineering organization. The role involves leading engineers in designing, implementing, and operating production systems that integrate LLMs and computer vision techniques for content automation, while also remaining involved in system design and architectural decisions. The manager will contribute to technical strategy, foster team development, and ensure alignment with product goals.

Senior Machine Learning Engineer focused on building and operating ML systems for personalized news content delivery across various platforms. The role involves designing and developing infrastructure for the full ML lifecycle, including data pipelines, feature engineering, training, and low-latency online serving. Key responsibilities include driving ML-driven solutions for recommendation systems, RAG, and autogenerated tagging, and ensuring scalable, impactful solutions.

Senior Software Engineer role focused on developing and deploying custom AI solutions using LLMs to automate business processes and improve business outcomes within the Disney Consumer Products - Licensing Technology organization. The role involves designing, configuring, and deploying models, integrating systems, and building web solutions, with a strong emphasis on Java, Python, and cloud technologies.

Senior Manager, Machine Learning Engineering to lead a team building and operating production ML systems for cross-media measurement, identity resolution, and audience development. Requires deep applied ML and ML engineering expertise, robust MLOps, and ownership of data pipelines for large-scale data. Must enforce privacy and governance.

Lead Machine Learning Engineer responsible for technical leadership of complex ML systems and data foundations. Builds predictive systems at scale for identity, audience, and cross-platform measurement using deep learning, genAI, or RAG. Architects scalable ML platforms, MLOps, and data foundations, ensuring privacy and compliance.

Lead Software Engineer to drive the development of scalable, intelligent systems that support licensing financial operations and AI-driven decision-making. This role will be instrumental in shaping the architecture and delivery of next-generation platforms that power financial licensing workflows and predictive analytics. Apply machine learning and AI techniques to optimize licensing workflows, forecasting, and compliance monitoring.

Principal Site Reliability Engineer responsible for leading and impacting SRE culture, advocating for service level management, and designing/building/supporting products and platforms. This role will lead the adoption of AI/LLM-assisted reliability engineering, architecting and operating AI-enabled capabilities, and optimizing system reliability through advanced analytics. The role requires expertise in evaluating GPT model families, engineering RAG systems, and automating infrastructure and operations.

Principal Site Reliability Engineer responsible for leading and impacting SRE culture, advocating for service level management, and designing/building/supporting products and platforms. This role will lead the adoption of AI/LLM-assisted reliability engineering, architecting and operating AI-enabled capabilities, and optimizing system reliability through advanced analytics. The role requires expertise in evaluating GPT model families, engineering RAG systems, and automating infrastructure and operations.

Senior AI Engineer to build AI core capabilities and tooling that accelerate teams across Ad Technology. This role blends backend engineering, LLM orchestration, and developer enablement, focusing on creating shared agents, initializers, registries, and reusable services and web applications to make AI adoption fast, safe, and cost-effective. The work emphasizes common design principles, guardrails, governance, observability, and evaluation to enable teams to deliver high-quality AI solutions quickly.