Senior Manager - Machine Learning

United Airlines•Chicago, IL

27d

About The Position

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. 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, DevOps, and business units to deliver impactful AI outcomes at scale.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering or a relevant field
7+ years of relevant experience
Experience leading ML Ops teams
Gen AI technology experience
Understanding of architecture and relevant tools like PySpark, MLflow, LangChain, AWS, or etc.
Must be legally authorized to work in the United States for any employer without sponsorship
Successful completion of interview required to meet job qualification
Reliable, punctual attendance is an essential function of the position

Nice To Haves

9+ years preferred
Experienced in platform and platform enablement. ML pipeline creation, management, feature engineering
5+ years of software development experience
2+ years of GenAI experience

Responsibilities

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 DataOps 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