Senior Staff ML Engineer — Personalization at Scale

Uber; United States; Hybrid; USD 267,000 - 297,000; Full time

Benefits offered by this job

Bonus program

Equity (stock)

401(k) plan

Job summary

Uber is seeking a Senior Staff Machine Learning Engineer to define the long-term ML strategy for Consumer Incentives. You will lead optimization, experimentation, and scalable systems, working with product, data science, and engineering leaders to deliver measurable business impact.

Work spans reinforcement learning, foundation models, and generative AI, with a focus on aligning technical decisions with growth and profitability. In-office collaboration is expected across major hubs.

Qualifications

• Masters or PhD in Computer Science, Engineering, Mathematics.
• 12+ years of ML experience on large-scale deep learning systems.
• Ownership of high-impact ML systems such as search, recommendations, personalization, targeting.
• Ability to set the technical strategy for a large organization and influence product roadmaps at the executive level.

Responsibilities

• Set long-term ML vision, architecture and roadmap for Consumer Incentives.
• Lead strategic initiatives from problem formulation to production deployment.
• Advance personalization and optimization to impact business metrics.
• Architect scalable end-to-end ML systems with reliable data pipelines and monitoring.
• Establish robust model evaluation, experiments and causal measurement.
• Translate advances in ML into practical products and align with business value.
• Mentor engineers and raise the quality of technical decisions.

Skills

ML leadership

Strategic thinking

Cross-functional

Mentorship

Product intuition

Education

Masters/PhD in CS

Job description

Uber is seeking a Senior Staff Machine Learning Engineer to define the long-term ML strategy for Consumer Incentives. You will lead optimization, experimentation, and scalable systems, working with product, data science, and engineering leaders to deliver measurable business impact.

Work spans reinforcement learning, foundation models, and generative AI, with a focus on aligning technical decisions with growth and profitability. In-office collaboration is expected across major hubs.