דרושים Staff Applied Scientist - Capacity & Efficiency Engineering ב-Uber ב-ארהב

You’ll tackle some of the most complex problems in applied machine learning: real-time pricing optimization, supply-demand balancing, and driver behavior modeling at unprecedented scale. Your work will involve cutting-edge techniques including causal inference, reinforcement learning, algorithmic game theory, and multi-objective optimization to solve challenges that don’t exist anywhere else in the industry.

As a Senior Staff ML Engineer reporting directly to the Engineering Director, you’ll drive technical strategy, mentor senior engineers, and establish ML engineering excellence across the Driver Pricing organization while solving problems that directly impact tens of billions of dollars in marketplace transactions.

Technical Leadership & Innovation

• Lead the design and implementation of advanced ML systems for dynamic pricing algorithms serving millions of drivers across 70+ countries around the world
• Architect real-time ML infrastructure handling 1M+ pricing decisions per second with sub-50ms latency requirements
• Drive breakthrough research in causal ML, reinforcement learning, algorithmic game theory, and multi-objective optimization for marketplace optimization with strategic agents
• Own end-to-end ML model lifecycle from research through production deployment and continuous optimization

Platform & Architecture

• Build scalable ML architecture and feature management systems supporting Driver Pricing and broader Marketplace teams
• Design experimentation frameworks enabling rapid testing of pricing algorithms using A/B, Switchback, Synthetic Control, and other experimental methodologies
• Establish ML engineering best practices, monitoring, and operational excellence across the organization
• Create platform abstractions that enable other ML engineers to iterate faster on pricing algorithms

Cross-Functional Impact

• Partner with Product, Operations, and Earner Experience teams to translate complex business requirements into ML solutions
• Collaborate with Marketplace Engineering and Science teams to productionize cutting-edge ML research
• Work with Platform Engineering teams to ensure ML systems meet reliability and performance standards
• Influence technical roadmaps across multiple teams through technical leadership and strategic thinking

Team Development

• Mentor and grow senior ML engineers, establishing technical standards and engineering culture
• Lead technical discussions and architecture reviews for complex ML systems
• Drive knowledge sharing and technical excellence across the Driver Pricing engineering organization

• PhD in Computer Science, Machine Learning, Operations Research, or related quantitative field OR Master’s degree with 12+ years of industry experience
• 10+ years of experience building and deploying ML models in large-scale production environments
• Expert-level proficiency in modern ML frameworks (TensorFlow, PyTorch, JAX) and distributed computing platforms (Spark, Ray)
• Deep expertise across multiple areas including: Deep Learning, Causal Inference, Reinforcement Learning, Multi-objective Optimization, Algorithmic Game Theory, and Large-scale Ads Ranking/Auction Systems
• Proven track record of leading complex ML projects from research through production with significant measurable business impact
• Strong programming skills in Python, Java, or Go with experience building production ML systems
• Experience with feature engineering, model serving, and ML infrastructure at scale (handling millions of predictions per second)
• Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives

• Marketplace or two-sided platform ML experience with understanding of supply-demand dynamics and pricing mechanisms
• Publications or patents in applied machine learning, particularly in areas relevant to optimization, pricing, or marketplace dynamics
• Experience with causal inference methodologies and their application to business problems with network effects
• Reinforcement learning experience in production environments with long-term optimization and strategic agent considerations
• Technical leadership experience including mentoring senior engineers and driving cross-team technical initiatives
• Experience with real-time ML systems requiring low-latency inference and high-throughput model serving
• Background in economics, operations research, or related quantitative disciplines with application to marketplace problems
• Experience with Ads ranking and auction systems with strategic bidding agents and real-time optimization

Required:

• Advanced Deep Learning and Neural Network architectures
• Scalable ML architecture and distributed model training
• Feature engineering and real-time feature serving
• ML model deployment, monitoring, and lifecycle management
• Statistical analysis and experimental design for ML systems
• Causal Machine Learning and causal inference methodologies
• Reinforcement Learning and Multi-Armed Bandits
• Multi-objective optimization and Pareto efficiency
• Algorithmic Game Theory for strategic agent modeling

Preferred:

• Personalization and ranking systems at scale
• Time series forecasting and demand prediction
• Graph-based ML for network effects modeling
• Experience with Ads ranking and auction systems

For New York, NY-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year.

About the Role

Your primary mission will be to architect a world-class search ecosystem for all Uber employees. This requires a dual-pronged technical strategy: leading the in-house development of novel AI components while also conducting deep evaluations of cutting-edge third-party solutions. You will be the key technical decision-maker on when to build and when to integrate.

- - - - What the Candidate Will Do ----

1. Architect the Ecosystem: Design and own the technical vision for Uber's next-generation enterprise search platform, creating a cohesive strategy that integrates first-party AI models with best-in-class third-party technologies.
2. Lead AI Implementation: Drive the exploration and implementation of advanced AI capabilities. This includes architecting conversational interfaces, enabling multi-step reasoning, and bringing agentic AI functionalities to our users, whether through in-house development or strategic integration.
3. Make Critical Build-vs-Buy Decisions: Conduct deep technical evaluations of industry-leading AI solutions. You will own the process of vetting vendors, prototyping integrations, and providing clear recommendations to leadership.
4. Lead and Mentor: Lead a world-class team of software and machine learning engineers. Provide technical guidance, conduct architectural reviews, and foster a culture of innovation and engineering excellence.
5. Build and Integrate: Remain hands-on, writing production-quality code for critical custom components, integration layers, and core platform infrastructure.
6. Collaborate and Influence: Work closely with senior leadership, product managers, and stakeholders across the company to define the roadmap and ensure your team's work aligns with Uber's strategic goals.

- - - - Basic Qualifications ----

1. Bachelor's degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.
2. 8+ years of professional experience in software engineering, with a proven track record of shipping complex.
3. Strong programming proficiency in Python and experience with systems programming languages (e.g., Go, Java, C++).
4. Significant experience in designing and building production systems leveraging machine learning, especially in Search, NLP, or Conversational AI.
5. 2+ years of experience in a formal or informal technical leadership role, including mentoring engineers and setting technical direction for a team.

- - - - Preferred Qualifications ----

1. Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
2. Experience architecting systems that involve Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and vector databases.
3. Demonstrated experience making strategic build-vs-buy decisions and integrating large-scale third-party software or SaaS platforms into a complex enterprise environment.
4. Experience with the architecture of AI agentic systems or complex, multi-step LLM-powered workflows (either through building or integration).
5. Expertise with both traditional search technologies (e.g., Elasticsearch) and modern semantic search paradigms.
6. Excellent communication skills, with the ability to articulate complex technical trade-offs to diverse audiences and influence technical strategy at a senior level.
7. A strong product intuition and a passion for creating user-centric AI experiences that solve real-world problems.

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .

About the Role

- - - - What the Candidate Will Do ----

1. Solve business-critical problems using a mix of generative AI, classical ML, and deep learning.
2. Build generative AI applications (e.g., conversational assistants, text summarization, multimodal experiences) using large language models and related architectures.
3. Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap for Applied AI initiatives.
4. Deliver high-quality, production-ready ML systems and infrastructure, from experimentation through deployment and monitoring.
5. Adopt best practices in ML development lifecycle (e.g., data versioning, model training, evaluation, monitoring, responsible AI).
6. Deliver enduring value in the form of software and model artifacts.

- - - - Basic Qualifications ----

1. Master or PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field and 2+ years of Software Engineering work experience.
2. Experience in programming with a language such as Python, C, C++, Java, or Go.
3. Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
4. Experience with SQL and database systems such as Hive, Kafka, and Cassandra.
5. Experience in the development, training, productionization and monitoring of ML solutions at scale.
6. Strong desire for continuous learning and professional growth, coupled with a commitment to developing best-in-class systems.
7. Excellent problem-solving and analytical abilities.
8. Proven ability to collaborate effectively as a team player.

- - - - Preferred Qualifications ----

1. Prior experience working with generative AI (e.g., LLMs, diffusion models) or multimodal AI and integrating such technologies into end-user products.
2. Exposure to audio ML and voice AI (STT, TTS, voice embeddings, etc.).
3. Experience in modern deep learning architectures and probabilistic models.
4. Machine Learning, Computer Science, Statistics, or a related field with research or applied focus on large-scale ML systems.

What You Will Do

1. Develop data-driven business insights and work with cross-functional customers to find opportunities and recommend prioritization of product, growth, and optimization initiatives.
2. Build statistical, optimization, and machine learning models for strategic insights as well as in production enhancements to the membership program.
3. Design and analyze experiments, present results that provide actionable recommendations.
4. Orient our teams around data-driven product development by driving the creation of logging, metrics, data visualization and diagnostic tools, and experimentation paradigms.
5. Define how our teams measure success, by developing metrics, in close partnership with cross functional partners.

- - - - Basic Qualifications ----

1. Ph.D., M.S. or Bachelor's degree in Statistics, Economics, Mathemathics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
2. 4+ years of industry experience as an Applied or Data Scientist or equivalent (or 2+ years with Ph.D.).
3. Background in at least one programming language (eg. R, Python, Java, Ruby, Scala/Spark or Perl)
4. Coding and SQL proficiency and ability to develop statistical analysis and algorithm prototyping in Python or R.
5. Ability to use Python, SQL, R or similar technologies to work efficiently with large data sets
6. Design experiments and interpret the results to draw detailed and actionable conclusions across a variety of key performance indicators

- - - - Preferred Qualifications ----

1. Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams
2. Experience leading key technical projects and substantially influencing the scope and output of others
3. Knowledge of experimental design and analysis or experience with exploratory data analysis and model development
4. Experience communicating qualitative research methods and findings to non-qualitative researchers
5. Track record of engaging senior leadership effectively to build understanding of and consensus for the viewpoints of the team
6. Solid theoretical and applied ML skills and a strong background in mathematics and stats
7. Solid Programming skills to prototype models in at least one of Python (preferably), R, Java, Go, Scala
8. Expert in one of the following areas: Deep learning, ML System Design, A/B experimentation design, Causal Inference
9. Experience of working with large dataset using Spark, Hive, HDFS is desired
10. Analyze large data sets to identify behavior trends among good users and bad actors, using statistics, data mining, and machine learning techniques
11. Thought leadership to drive multi-functional projects from concept to production

For San Francisco, CA-based roles: The base salary range for this role is USD$183,000 per year - USD$203,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link .

What the Candidate Will Do:

1. Serve as a technical leader on our most demanding, cross-functional projects.
2. Functionally decompose complex problems into simple, straight-forward solutions.
3. Mentor and grow other eng leaders on the team and in the org
4. Possess expert knowledge in performance, scalability, enterprise system architecture, and engineering best practices.
5. Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues and helping managers guide the career growth of their team members
6. Exert technical influence over the App Experience team, increasing the team's productivity and effectiveness by sharing your deep knowledge and experience. You are a visionary leader, builder and an operator.
7. Balance technical leadership and savvy with strong business judgment to make the right decisions about technology choices.

Basic Qualifications:

1. Minimum 7 years of software engineering experience
2. Strong product-focused engineer with past consumer product expertise
3. Experience leading teams of other product-minded engineers, while also contributing to the roadmap across IC dimensions
4. Mastery of object-oriented design, data structures, distributed systems, and algorithms

Preferred Qualifications:

1. Deep hands-on technical expertise in at least one major technical area: large scale full stack development, building complex distributed systems, launching complex UX overhauls
2. Experience working with personalization and recommendation systems to craft rich, cohort-based experiences for users
3. Domain knowledge of marketplace systems
4. Strong cross-functional collaboration and team bar-raising skills

For New York, NY-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$223,000 per year - USD$248,000 per year.

About the Role

As a TPM in this role, you will own and drive large, complex cross-team technical initiatives for Platform Engineering in one or more areas of Capacity, Platform Efficiency, Reliability, Cloud, modernization, and Security. Only a few and very high-impact programs at Uber will have TPMs assigned to them, and those programs require a cross-functional engagement among stakeholders and leadership to succeed.

Specifically, you will own the problem space, work closely with engineering leads to conceptualize solutions, organize/align multiple engineering teams for delivery, and drive execution leveraging KPIs. You will look for ways to standardize, ensure execution is efficient and consistent, and set accountability by tracking progress, resolving dependencies, identifying issues, evaluating risks, and communicating status to leadership and stakeholders. You will intrinsically act as a coach to engage and energize the product and engineering teams to achieve important goals.

What you will do:

1. Manage complex and large-scale cross-team, cross-functional initiatives. Interface with engineering managers and engineers to estimate work efforts, define milestones, and manage resources. Track progress, resolve dependencies, evaluate risks, and communicate status to upper management and program stakeholders. Engage and energize ‌project teams to achieve aggressive goals.
2. Proactively remove obstacles to drive momentum and progress. Identify communication gaps, manage issue escalations, and provide support to teams balancing competing priorities. Drive results proactively.
3. Understand technical implementation at the architecture level and propose technical alternatives when necessary. Ask questions that clarify priorities and push the team to be highly effective. Develop broad domain and technical knowledge.
4. Understand product compromises. Have the end customer in mind and be able to bridge between end customer and technical aspects with a data-driven approach to increase our efficiency.
5. Help manage the cross-functional strategic planning and business planning process, leading conversations with key stakeholders to develop concepts for strategic initiatives, and developing those into detailed program / project plans (including roadmaps, funding planning, resource planning, clear objectives, measurable outcomes, and hiring goals).
6. Continuously improve and scale the organization. Implement continuous improvements to the engineering organization and the program management process. Share program/project process frameworks, tools, and best practices that can be adopted throughout the organization. Improve product development and delivery. Mentor project management skills within our engineering leadership team.

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .

About the Role

- - - - What the Candidate Will Do ----

1. Use data to understand product performance and to identify improvement opportunities.
2. Build statistical, optimization, and machine learning models for a range of applications in the pricing and incentives algorithms space.
3. Design and execute product experiments and interpret the results to draw detailed and actionable conclusions.
4. Present findings to senior management to inform business decisions.
5. Collaborate with cross-functional teams across disciplines such as product, engineering, operations, and marketing to drive system development end-to-end from ideation to productionization

- - - - Basic Qualifications ----

1. Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
2. 2+ years of experience as an Applied or Data Scientist or equivalent (can be also as part of Ph.D training).
3. Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics.
4. Experience in experimental design and analysis.
5. Experience with exploratory data analysis, statistical analysis and testing, and model development.
6. Ability to use Python to work efficiently at scale with large data sets.
7. Proficiency in SQL.

- - - - Preferred Qualifications ----

1. 2+ years of industry experience.
2. Experience in algorithm development and prototyping.
3. Experience in pricing optimization.
4. Experience with productionizing algorithms for real-time systems.
5. Well-honed communication and presentation skills.

For New York, NY-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year.