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About This Role
About the Role
Uber Direct powers fast, reliable delivery for enterprise retailers and local businesses by leveraging Uber's world\-class logistics network. As a Senior Machine Learning Engineer on the Uber Direct team, you will define and build intelligent systems that improve operational efficiency, customer experience, and predictive capabilities in real\-time logistics at global scale.
You'll partner closely with Product, Data Science, and Engineering teams to design, deploy, and continually enhance machine learning\-driven solutions that power core decision\-making across the delivery lifecycle. Your work will directly influence key marketplace and logistics metrics across millions of global deliveries.
What You'll Do* Develop High\-Impact ML Solutions: Design, build, and productionize machine learning models that solve critical logistics problems such as ETA prediction, demand forecasting, dispatch optimization, anomaly detection, and delivery quality improvements.
- Own the End\-to\-End ML Lifecycle: Lead projects from problem definition and data exploration through feature engineering, model development, evaluation, deployment, monitoring, and iteration.
- Build Scalable ML Systems: Develop robust data pipelines, feature stores, training workflows, and model serving infrastructure that support both real\-time and batch inference at scale.
- Drive Business Impact: Define success metrics, run experiments, and rigorously evaluate model performance to ensure measurable improvements to KPIs such as Completion Rate, On\-Time Rate, and Defect Rate.
- Collaborate Cross\-Functionally: Work closely with Product Managers, Data Scientists, Operations, and Backend Engineers to translate business problems into scalable ML solutions.
- Technical Leadership \& Mentorship: Provide technical direction, establish best practices in ML and MLOps, and mentor engineers across the team.
Basic Qualifications* Bachelor's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field, or equivalent practical experience.
- 5\+ years of experience building and shipping production\-grade machine learning systems.
- Strong proficiency in Python , plus experience with at least one additional programming language (e.g., Go, Java, C\+\+, Scala).
- Hands\-on experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or Scikit\-Learn .
- Demonstrated experience deploying, monitoring, and maintaining ML models in production environments.
- Solid understanding of statistics, feature engineering, model evaluation methodologies, and experimental design.
- Strong software engineering fundamentals, including data structures, algorithms, and system design.
Preferred Qualifications* Master's or PhD in Machine Learning, Computer Science, Statistics, or related field.
- Experience building large\-scale ML systems in a high\-throughput, low\-latency production environment.
- Background in logistics, marketplace systems, forecasting, optimization, recommendation systems, or time\-series modeling.
- Experience with distributed data processing frameworks (e.g., Spark, Hive) and streaming systems (e.g., Kafka).
- Familiarity with MLOps tooling such as Airflow, Kubeflow, MLflow, feature stores, and CI/CD pipelines for ML workflows.
- Experience with A/B testing, experimentation frameworks, and causal inference.
- Proven ability to optimize ML systems for scalability, reliability, observability, and latency.
- Experience mentoring engineers and contributing to technical strategy.
Success Attributes
Machine Learning Depth: Strong foundation in ML theory and applied modeling, with the ability to balance trade\-offs between accuracy, interpretability, and system performance.
Engineering Excellence: Ability to design and implement scalable, maintainable ML systems that operate reliably in production.
Ownership Mindset: End\-to\-end accountability for model quality, system health, and business impact.
Cross\-Functional Leadership: Ability to influence and collaborate effectively with Product, Science, and Engineering stakeholders.
Impact Orientation: Focus on delivering measurable improvements to core business metrics through data\-driven solutions.
Why Uber Direct?
At Uber Direct, you'll help shape the future of logistics through data\-driven intelligence at global scale. Your work will directly power the technology behind enterprise delivery and impact millions of customers worldwide. Join a team where experimentation, innovation, and ownership are core to our engineering culture.
For New York, NY\-based roles: The base salary range for this role is USD$202,000 per year \- USD$224,000 per year. For San Francisco, CA\-based roles: The base salary range for this role is USD$202,000 per year \- USD$224,000 per year. For all US locations, 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. More details can be found at the following link https://jobs.uber.com/en/benefits.
Salary Context
This $202K-$224K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 Required
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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($213K) sits 18% above the category median. Disclosed range: $202K to $224K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Uber AI Hiring
Uber has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Sunnyvale, CA, US, New York, NY, US. Compensation range: $180K - $224K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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