Lead Machine Learning Engineer

Job-Zusammenfassung:

At Disney Experiences Technology, our team creates world-class immersive and digital experiences for the Company’s vacation brands, Disney’s Parks and Resorts worldwide, Disney Cruise Line, Aulani, A Disney Resort & Spa, and Disney Vacation Club. The Disney Experiences Technology team is responsible for the end-to-end digital and physical Guest experience for all technology & digital-led initiatives across the Attractions & Entertainment, Food & Beverage, Resorts & Transportation, and Merchandise lines of business as well as other initiatives including the MyDisneyExperience app and Hey, Disney! 

The team is seeking a results-oriented and hands-on Lead Machine Learning Engineer to design, develop, and deploy high-impact AI/ML solutions that drive measurable business value across our entertainment company. In this role, you will lead complex, cross-functional projects with a strong emphasis on reuse, scalability, reliability, and performance.

The Lead ML Engineer will report to the ML Engineering Manager.

About The Role & Team:

The DXT AI Technology Platform team is responsible for building an AI enablement platform for the DX segment that provides streamlined AI & Generative AI capabilities for the segment to build solutions around and on top of.  The Lead Machine Learning Engineer will design, develop, implement enterprise grade and robust AI/ML solutions, including agentic systems, multi-modal models, RAG, and Responsible AI applications.

This position is in office.

What You’ll Do:

• Develop sophisticated, production-scale AI systems, including multi-step agentic workflows and multi-agent orchestration platforms.
• Build tools & agents with advanced capabilities in reasoning, planning, and adaptive tool utilization to address complex business challenges.
• Drive complete ownership of the AI/ML lifecycle – encompassing implementation, comprehensive testing, deployment, and continuous operational monitoring – delivering projects on schedule and to specification.
• Produce high-quality, maintainable code for model training pipelines, evaluation frameworks, and inference services that meet production standards.
• Partner strategically with cross-functional stakeholders including product leaders, data scientists, application teams, vendors, and partners to align on requirements, iterate on solutions, and deliver successful outcomes.
• Provide hands-on technical leadership, driving architectural decisions and championing best practices across AI development, LLMOps, quality assurance, and production deployment.
• Design and implement responsible AI frameworks including hallucination detection, safety guardrails, comprehensive evaluation systems, and observability infrastructure to ensure model reliability, accuracy, and ethical deployment.
• Establish comprehensive evaluation frameworks for Large Language Models and agent-based systems, measuring model quality, task success rates, safety compliance, and operational effectiveness.
• Proactively identify and resolve technical blockers that could impact project timelines or deliverables.
• Communicate technical strategy and progress to executive leadership and key stakeholders with clarity and confidence.
• Engage directly in development and problem-solving, particularly on high-complexity technical challenges, to maintain project velocity and quality.
• Drive innovation through research and experimentation with emerging AI technologies and frameworks, evaluating and integrating new capabilities that advance our platform.

Basic Qualifications:

• 7+ years of proven expertise in designing, building, and deploying AI/ML solutions at scale, with 1-2 years of production experience in Generative AI technologies.
• Strong foundation in machine learning including statistical modeling, supervised and unsupervised learning algorithms.
• Advanced skills in prompt engineering with deep understanding of optimization techniques and best practices for LLM interactions.
• Expert-level programming proficiency in Python and AI/ML development ecosystems.
• Deep expertise in modern AI frameworks including LLM application development and agentic systems (LangChain, CrewAI, or similar).
• Comprehensive MLOps experience with hands-on implementation of CI/CD pipelines, model monitoring, versioning, and lifecycle management for both models and agent-based systems.
• Production deployment experience on major cloud platforms (AWS, Azure, or GCP) with demonstrated ability to architect and scale cloud-native ML solutions.
• Versatile ML skillset spanning traditional techniques (classification, regression, clustering) and cutting-edge deep learning approaches.
• Production-grade generative AI experience deploying and maintaining LLMs and multi-modal models in live environments.
• Exceptional analytical capabilities with a track record of solving complex technical problems and thriving in ambiguous, rapidly-evolving situations.
• Proficiency with industry-standard ML libraries including PyTorch, TensorFlow, Scikit-learn, NumPy, and Pandas.
• Outstanding communication and collaboration skills with ability to translate complex technical concepts for diverse audiences and drive cross-functional alignment.
• Success partnering across organizational levels from individual contributors to senior leadership, building trust and delivering results.
• Proven ability to influence and lead in matrix organizations where collaboration and relationship-building are essential to achieving outcomes.

Preferred Qualifications:

• Experience with vector databases and embedding technologies.
• Specialized expertise in AI safety and responsible AI using evaluation tools such as Arize, Langfuse, TruLens, or equivalent platforms for hallucination detection, bias mitigation, and model performance assessment.
• Experience with advanced ML techniques including reinforcement learning from human feedback (RLHF), model fine-tuning (LoRA, QLoRA), retrieval-augmented generation (RAG), or model distillation and optimization.
• Familiarity with real-time data processing and streaming architectures using technologies such as Apache Kafka, Google Pub/Sub, AWS Kinesis, or Azure Event Hubs for building responsive ML systems.

Required Education:

• Bachelor's degree in Computer Science, Machine Learning, Mathematical Sciences, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.

Preferred Education:

• Master's degree or Ph.D in Artificial Intelligence, Machine Learning, Mathematical Sciences, Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.