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About the Role
We are looking for a senior operations leader to lead two increasingly interconnected parts of our global commercial operating model: our offshore Digital Sales Center (DSC) operations and our Agentic AI go-to-market strategy.
This role will be responsible for building and operating a scaled commercial engine that combines offshore talent, automation, and AI to improve seller productivity, increase commercial capacity, and reduce the cost to serve merchants globally.
The leader will own Central Operations for DSC, establishing the operating model, performance management, capacity planning, vendor strategy, and global standards required to run offshore commercial operations at scale. In parallel, they will lead the operational strategy and GTM for Agentic AI across both autonomous AI , where agents execute commercial work independently, and augmented AI , where AI increases the effectiveness and productivity of our sellers.
This is not a traditional sales operations or AI program management role. The mandate is to redesign how commercial work gets done, determine which work should sit with sellers, offshore teams, or AI, and build the operating systems required to scale that model globally.
What You'll Do
Lead Global DSC Central Operations
- Own the global operating strategy and Central Operations model for offshore Digital Sales Centers supporting Delivery.
- Establish standardized operating mechanisms across regions, including capacity planning, workforce allocation, performance management, forecasting, productivity, quality, and operational governance.
- Partner with regional Commercial and Operations leaders to determine where offshore capacity can most effectively support acquisition, onboarding, expansion, retention, and other merchant lifecycle motions.
- Build a rigorous performance framework connecting DSC activity to commercial outcomes, including productivity, conversion, first trips, quality, cost to serve, and ROI.
- Lead the evolution of DSC from a predominantly people-based operating model toward a more automated, AI-enabled commercial engine.
- Develop vendor and location strategies that optimize for capability, quality, scalability, and economics.
Lead Agentic AI GTM & Automation Operations
- Own the operational strategy and GTM framework for Agentic AI across Delivery Commercial Operations.
- Build a portfolio strategy spanning autonomous AI agents that execute defined commercial workflows and augmented AI capabilities that improve seller decision-making, preparation, execution, and follow-up.
- Identify and prioritize commercial workflows for AI based on revenue opportunity, productivity impact, feasibility, merchant experience, and cost reduction.
- Translate AI capabilities into scalable operating models, moving successful use cases from experimentation through pilot, production, and global adoption.
- Establish clear measurement frameworks for AI, including adoption, utilization, seller time saved, incremental commercial output, conversion, quality, and ROI.
- Partner closely with Product, Engineering, Data Science, Sales, Enablement, Legal, Privacy, and regional Operations teams to ensure AI solutions are commercially useful, operationally scalable, and responsibly deployed.
- Build the operating mechanisms required to manage an expanding portfolio of AI agents and capabilities, including prioritization, experimentation standards, performance monitoring, governance, and lifecycle management.
Redesign the Commercial Operating Model
- Develop a clear framework for determining which commercial activities should be seller-led, offshore-led, AI-augmented, or fully autonomous .
- Continuously identify opportunities to automate manual work, simplify workflows, and redirect seller capacity toward higher-value merchant interactions.
- Connect workforce planning and AI planning so that automation translates into measurable capacity and productivity benefits rather than incremental tooling.
- Develop the multi-year roadmap for evolving the DSC and seller operating model as AI capabilities mature.
- Influence senior leaders across Commercial, Operations, Product, and Technology to make the organizational and process changes required to capture the value of automation.
Build and Lead a High-Performing Team
- Lead a global team responsible for DSC Central Operations and AI GTM/automation.
- Set a high bar for analytical rigor, operational execution, commercial judgment, and accountability.
- Build strong relationships across regional and global teams while maintaining clear single-threaded ownership of global standards and strategy.
- Develop leaders who can independently own complex, cross-functional programs and operating areas.
Basic Qualifications* 10+ years of experience in Commercial Operations, Sales Operations, Strategy & Operations, scaled sales, business operations, consulting, or a related field.
- 5+ years of experience leading teams and complex cross-functional initiatives.
- Demonstrated experience designing and operating large-scale commercial or operational systems across multiple markets or regions.
- Strong understanding of sales funnels, commercial productivity, capacity planning, performance management, and unit economics.
- Experience translating emerging technology or automation capabilities into operational and business outcomes.
- Strong analytical capability and ability to use data to diagnose performance, prioritize investment, and make resource allocation decisions.
- Demonstrated ability to influence senior stakeholders across Commercial, Product, Engineering, Finance, and Operations without direct authority.
Preferred Qualifications* Experience leading offshore, outsourced, BPO, digital sales, or scaled commercial operations.
- Experience deploying AI, automation, conversational AI, or agentic technologies into commercial workflows.
- Experience managing the transition from manual operations toward increasingly automated operating models.
- Experience with enterprise CRM, sales engagement, workflow automation, or AI platforms.
- Experience managing vendor ecosystems and significant external operating spend.
- Experience operating within a complex global matrix with both regional and centralized teams.
What Success Looks Like
Success in this role means creating a materially more productive commercial operating model. Offshore operations are globally standardized, measurable, and increasingly automated. AI moves beyond pilots into repeatable commercial workflows with demonstrated adoption and ROI. Sellers spend less time on low-value work and more time on activities where human judgment and merchant relationships matter.
Ultimately, this leader will help build a commercial organization where human sellers, offshore teams, and AI agents operate as one integrated system , increasing commercial capacity and merchant impact without requiring equivalent growth in headcount.
For New York City, NY-based roles: The base salary range for this role is USD $203,000 per year - USD $225,000 per year.
You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.
Ready to Ride?
This isn't the kind of place where you follow a playbook - it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves - we'd love to hear from you.
You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.
Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.
Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
Salary Context
This $203K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Uber, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $203K to $225K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Uber AI Hiring
Uber has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Sunnyvale, CA, New York, NY, US, New York, NY. Compensation range: $225K - $225K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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