Senior Manager - Machine Learning

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United's Digital Technology team is comprised of many talented individuals all working together with cutting‑edge technology to build the best airline in the history of aviation. Our team designs, develops and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions.

Job overview and responsibilities

We’re seeking a strategic and technically strong Senior Manager, Machine Engineering to lead our enterprise ML and GenAI platform. This individual will drive architecture, development, and operations of our ML engineering and GenAI systems, enabling scalable and responsible AI solutions across the business. The ideal candidate brings a deep understanding of ML infrastructure and MLOps, combined with hands‑on or architectural experience in LLMs, RAG pipelines, and GenAI application integration.

You’ll lead a team of ML engineers and collaborate cross‑functionally with Data Science, Data Engineering, Dev Ops, and business units to deliver impactful AI outcomes at scale.

• Strategic Leadership & Platform Ownership:
  • Define and execute the ML/GenAI platform strategy aligned with enterprise digital transformation objectives
  • Hands‑on experience leading an ML Generative AI
  • Own the platform roadmap, architecture decisions, and budget planning to scale AI capabilities across the enterprise
  • Collaborate with CDO, CIO, and senior stakeholders to identify, prioritize, and fund impactful AI/GenAI investments
  • Represent the ML Center of Excellence (COE) in cross‑functional meetings and strategic planning forums
  • Serve as the primary liaison between the COE and business units, effectively communicating technical capabilities and business impact
• GenAI & LLM Strategy:
  • Lead initiatives around LLMs and foundation models (e.g., OpenAI, Anthropic, Hugging Face, Cohere)
  • Design and operationalize GenAI pipelines (e.g., RAG, prompt orchestration, fine‑tuning, and safety guardrails)
  • Build and deploy secure, scalable GenAI applications with a strong emphasis on privacy, safety, and compliance
  • Integrate LLMs into enterprise workflows, such as copilots, document summarization, intelligent assistants, and domain‑specific Q&A systems
• ML Engineering & MLOps:
  • Design, manage, and monitor the enterprise ML engineering platform, ensuring scalability, reliability, and automation
  • Take ownership of ML production systems with a focus on CI/CD, observability, and resilient architecture
  • Develop robust MLOps processes to monitor model performance, detect drift, and automate retraining and validation
  • Build and maintain tools and frameworks to govern ML models for compliance, bias, versioning, traceability, and auditability
• Data Engineering & Feature Platforms:
  • Design and implement feature engineering and data pipelines to deliver high‑quality training data and inference‑ready datasets
  • Partner with data scientists and engineers to create reusable, production‑grade feature stores and pipelines
  • Solve complex data ingestion, transformation, and governance challenges in collaboration with data platform and Data Ops teams
  • Develop integrated ML/AI solutions on enterprise analytics platforms (e.g., Palantir Foundry, Databricks)
• Team Leadership & Talent Development:
  • Hire, mentor, and grow a high‑performing ML engineering team with a focus on innovation, execution, and impact
  • Provide technical mentorship and guidance to ML engineers and data scientists, ensuring high standards in design and implementation
  • Promote a culture of continuous learning, experimentation, and operational excellence
  • Building auto‑scaling ML systems
• ML Engineering & MLOps:
  • MLflow, KServe, Sage Maker, Vertex AI, Databricks, etc.
• GenAI:
  • LLM providers (OpenAI, Anthropic), Hugging Face, Lang Chain/Llama Index
• Data Infra:
  • Spark, Kafka, Delta Lake, etc.
• Dev Ops:
  • Kubernetes, Jenkins, Git Ops, Terraform

What’s needed to succeed…