Machine learning engineer Jobs in Burbank, CA

Senior Manager, AI & Machine Learning

Disney Financial Insights (D-Fi) is a new platform reimagining how Finance teams and budget owners across Disney interact with financial data replacing manual pulls, static reports, and email-driven approvals with one AI-powered, orchestrated experience built on top of Disney financial systems.

We're hiring a Senior Manager, AI & Machine Learning, as the founding engineering leader for this AI framework: a hands-on manager and technical lead who will build and run a global engineering team based in Buenos Aires AR, delivering the AI/ML layer, the intelligent data foundation, software workflows/orchestration, and natural-language experience that sits at the center of D-Fi.

This is a build-from-scratch mandate you'll be shaping team structure, engineering practices, and technical architecture from an early stage (MVP in progress), not inheriting a mature system.

This role is the primary technical point of contact for finance stakeholders & cross-functional partners and is accountable for translating the platform roadmap into a shipped, governed, production-grade system.

As the Senior Manager, AI & Machine Learning, you'll be setting architecture, reviewing design decisions, and staying hands-on enough to credibly evaluate trade-offs your team brings you. This role is also expected to be hands-on stepping in on implementation, unblocking technical issues in real time, and reviewing code with the depth that comes from doing the work yourself, not just overseeing it.

Responsibilities include:

• Own technical direction end-to-end. Set architecture and technical strategy for the AI/ML and data layers underpinning D-Fi, ensuring every capability variance analysis, scenario modeling, the natural-language experience is built on a governed, semantic data foundation rather than one-off pipelines.
• Be the stakeholder-facing technical lead. Act as the primary engineering liaison to finance business partners and leadership translating challenging business needs into a scoped, buildable technical roadmap, and representing engineering trade-offs credibly to technical and non-technical partners and leaders.
• Own the AI/LLM roadmap. This is the highest-visibility and most sensitive capability on the platform you'll guide its direction end-to-end to build and maintain trust in AI-generated financial answers.
• Enforce governance by design. Ensure role-based access control (RBAC) is enforced at the data model level not the application level across all personas (Finance, Budget Owners, and any future personas), so speed and automation never come at the cost of data governance.
• Set the bar for engineering quality and delivery. Establish practices for code review, testing, model evaluation, and release management across a geo diverse team and multiple disciplines.
• Own the build vs. integrate decisions. Guide when the team builds custom capability versus integrates with existing systems (SAP, Cognos, EPM, Data Marketplace, NetDocs, Coupa, Clarity)
• Champion adoption broadly. Partner with the business finance transformation leads on driving adoption across the enterprise both finance workers and budget owners.

Required Qualifications & Skills:

• 10+ years of software and/or ML engineering leadership experience, with a strong track record of owning technical direction and architecture for complex systems end-to-end.
• Demonstrated experience architecting and shipping production LLM/AI applications not just prototypes.
• Production experience with LLM application architecture RAG pipelines, prompt engineering, and context/retrieval design at scale
• Working knowledge of LLM orchestration frameworks (e.g., LangChain, LlamaIndex, or equivalent) and vector databases/retrieval stores (e.g., Pinecone, Weaviate, pgvector, or Snowflake Cortex Search)
• Strong data modeling background, ideally including semantic/governed data layers (not just raw pipelines)
• Ability to read, write and review Python code
• Working knowledge of modern web/full-stack language or framework sufficient to guide UI-layer architecture decisions
• Familiarity with MLOps practices model monitoring, retraining pipeline design, containerization, and CI/CD for ML systems
• Experience working across distributed, cross-timezone engineering teams, with the judgment to know what belongs in synchronous vs. asynchronous workflows.
• Comfort operating as the primary technical voice in front of senior business stakeholders able to translate finance/business problems into technical scope, and technical trade-offs into business language.

Preferred Qualifications:

• Background building internal tools for finance organizations is a plus, but not required genuine curiosity about the domain is.
• Exposure to ERP/EPM platforms such as SAP, Oracle EPM, or Cognos, and/or experience integrating custom applications against enterprise system APIs
• Experience working alongside or evaluating third-party/consulting-delivered engineering work, with the judgment to assess technical quality and architectural fit quickly

Required Education:

• Bachelor's degree in Computer Science, Engineering, or related field; advanced degree a plus but not required given equivalent experience.

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