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About the role and team
Uber AI Solutions (UAIS) is a startup inside Uber, building the data and evaluation infrastructure behind the next generation of AI. The models making headlines are only as good as the data and feedback they learn from, and that is the work we do.
We are an agile team moving at startup speed with the full weight of Uber behind us: global reach, world class infrastructure, and one of the largest human networks in the world. We pair that network with machine learning to produce the high quality, model ready data that the most advanced AI teams on earth depend on. Our work spans every modality, from text, images, and video to audio and physical AI and AVs, and reaches across dozens of countries.
This is a place for builders who want real ownership. Here you own a problem, not a ticket: you take a vertical from start to finish, the machine learning, the backend systems, and the product, and drive it from a blank page to something the best customers in AI rely on every day. You work shoulder to shoulder with product managers and other exceptional engineers, go as deep as the problem demands, and make the calls that decide whether your area wins. It is the closest thing to running your own company, with a rocket strapped to it.
If you want your work to show up in the AI systems shaping the world, and you want to build it like a founder, this is the team.
What you'll do* Build the ML and backend systems that make the growth real and durable, going deep on the hardest problems yourself.
- Own your vertical end to end: set the direction, find where the growth is, and drive the business metrics that matter, such as revenue, customers, and market reach.
- Move fast and decisively in ambiguity, making the strategic and technical calls that decide whether the business wins.
- Be a trusted technical partner in the most important customer relationships, using them to grow the account and shape what you build next.
- Build the team and raise the bar so the business can keep scaling.
Basic Qualifications* Master's or PhD in Computer Science, Machine Learning, Electrical Engineering, Mathematics, or a related discipline.
- 7\+ years building and shipping products powered by ML, with a history of owning both the technology and the business results for a significant product area.
- Proven track record of growing a business, not just running it: you have driven significant, measurable business growth such as revenue, adoption, or market expansion.
- Deep expertise in both machine learning systems and backend distributed systems, with the ability to build production systems that scale as the business grows.
- A track record of finding the highest leverage opportunities in an ambiguous space and driving them to real business impact.
- Exceptional communication and leadership skills, with the ability to align customers, product, and business leadership around a growth agenda.
Preferred Qualifications* You have taken a product or business line from early stage to meaningful scale, ideally in a startup or startup like environment, owning revenue and growth throughout.
- Experience as the owner of an applied AI product domain, such as real world data, agentic environments, evaluation, or multimodal and multilingual ML, where you drove significant business outcomes.
- Expertise in GenAI and LLM systems at production scale, spanning architecture, evaluation, safety, and responsible deployment.
Depth in a domain central to UAIS such as physical world and physical AI data, agentic RL environments and evaluation, multilingual and audio, or robotics and egocentric data, and a track record of owning strategic customer relationships.
*
For San Francisco, CA\-based roles: The base salary range for this role is USD $267,000 per year \- USD $297,000 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 $267K-$297K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($282K) sits 31% above the category median. Disclosed range: $267K to $297K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Uber AI Hiring
Uber has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Sunnyvale, CA, US, San Francisco, CA, US. Compensation range: $190K - $297K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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