Top Machine Learning Jobs

Lead product vision and execution for a portfolio of ML-powered personalization and insights products. Translate research and analytics into roadmaps, run experiments, partner with data science and engineering, ensure privacy/fairness, enable self-serve platforms, and measure outcomes across marketing, servicing, assistants, and financial management.

Designs, develops, tests, operates, and maintains production software with full-stack ownership. Builds scalable, reliable, high-performing applications and complex APIs, with emphasis on backend and machine learning systems, GenAI, ML platforms, and embeddings. Conducts code reviews, mentors engineers, analyzes failures, improves reliability, and collaborates with Product, Design, and partner engineering teams using agile and DevOps practices.

Develop and improve machine learning systems for autonomous driving perception. Responsibilities include creating scalable ML methods, analyzing real-world system behavior, diagnosing root causes, enabling distributed fine-tuning, preventing production regressions, monitoring deployed models, building automated debugging tools, and developing evaluation metrics and recipes. The role requires Python or C++, PyTorch or JAX, and at least three years of machine learning or computer vision experience.

Build customer-facing data products for commercial vehicle customers, including forecasting, pricing signals, recommendations, matching, ranking, enrichment, and market intelligence. Apply LLMs and machine learning to messy structured and unstructured data, while creating evaluation, monitoring, and guardrail systems. Design ingestion, transformation, feature, entity-resolution, and data-quality pipelines. Partner with product, design, customers, and commercial teams, define success metrics, ship reliable solutions, and mentor analysts and engineers.

Own evaluation systems and model-quality improvements for a generative content platform producing AI-generated ecommerce content. Build datasets, rubrics, automated judges, quality gates, and labeled training data from brand feedback. Quantify qualitative improvements, establish approval thresholds, and drive fine-tuning or retrieval experiments into production. The role requires strong software and system design skills, production service ownership, formal machine learning or statistics training, and experience measuring correctness in non-deterministic systems.

Develops predictive models and data-driven solutions by analyzing complex datasets and applying advanced statistical and machine learning techniques. The role communicates insights to stakeholders, collaborates with cross-functional teams, and integrates analytics into business strategy. It requires extensive data analysis experience, an active TS/SCI clearance with polygraph, and U.S. citizenship. The position is onsite in Herndon, Virginia, with less than 10% travel.

Develop and deploy perception, generative AI, and online mapping algorithms for L4 autonomous trucks. Responsibilities include building robust real-time software for safety-critical systems, integrating maps and vehicle sensors, improving mapping performance, leading features from concept through delivery, and coordinating stakeholders. The role requires deep learning model development and deployment, applied research, machine learning algorithm design, Python, and C++, with sensor fusion or online mapping experience preferred.

Develop and implement statistical and machine learning trading strategies and quantitative models. Design optimization algorithms for portfolio construction and explore new research opportunities within derivatives and market behavior.

Build scalable machine learning pipelines, data and training infrastructure, distributed processing workflows, and reusable software systems for foundation-model research. Own codebase health, testing, reliability, maintainability, and performance across ML workflows. Collaborate with researchers and engineers to translate model and experiment requirements into robust systems supporting large datasets, large models, multimodal data, video, robotics, and embodied AI.

Develop advanced autonomy algorithms for physical AI, including data mining, deep scene understanding, causal modeling of ego-vehicle behavior, and robust identification of complex driving edge cases. Deploy algorithmic solutions for real-world, safety-critical autonomous systems and help establish high technical standards within Uber’s AV Labs team.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Develop machine learning algorithms and scalable AI systems for autonomous driving. Responsibilities include end-to-end ML implementation, sensor dependency management, dataset collection and auto-labeling, system architecture, and production deployment in safety-critical environments. The role collaborates with platform, product, security, cloud, compute, and big data engineering teams while providing technical leadership.

Build and operate production machine learning and backend systems for AI data and evaluation products. Own a business vertical end to end, from identifying opportunities and setting direction to delivering revenue-generating products. Partner with customers, product managers, and engineers; drive adoption, customer value, and execution quality. Apply machine learning across multimodal data, LLM evaluation, physical-world data, robotics, audio, video, and related domains.

Develop autonomy algorithms and foundation models that extract semantic insights from complex driving data. Design scalable machine learning systems, manage sensor dependencies, optimize datasets through data processing and auto-labeling, and deploy models into production. Collaborate with platform, product, and security engineering teams while providing technical leadership. The role requires expertise in Python, Linux, modern ML frameworks, and preferably autonomous driving, safety-critical systems, C++, and high-performance computing.

Lead the technical direction and development of Uber’s real-time marketplace matching optimization systems. Build and productionize machine learning and decision systems using reinforcement learning, deep learning, causal inference, optimization, and control methods. Formulate ambiguous problems, develop behavioral models, create simulation and experimentation frameworks, and deploy scalable online inference systems. Collaborate cross-functionally, lead 0-to-1 initiatives, improve marketplace performance, and mentor engineers.

Lead the technical direction for machine learning algorithms and foundation models that analyze autonomous driving data. Develop semantic modeling, multimodal scene understanding, causal behavior modeling, and data-mining systems for L4 autonomy. Mentor senior engineers, influence multiple teams, and partner with infrastructure groups to deploy scalable evaluation platforms. Requires expert Python, Linux, and modern ML framework skills, with substantial experience in machine learning, robotics, or autonomous systems.

Develop autonomy algorithms that extract semantic meaning from complex urban driving scenarios and enrich AV data. Design onboard systems, manage sensor dependencies, define evaluation datasets and metrics, and collaborate with platform, product, and security engineering teams to deploy machine learning techniques into production.

Develop autonomy algorithms that add semantic understanding to real-world driving data. Design onboard systems, manage sensor dependencies, define evaluation metrics, and build scalable production solutions for safety-critical autonomous vehicle systems. Collaborate with platform, product, and security engineering teams to deploy onboard systems.

Lead machine learning engineering for fraud prevention and account integrity. Shape technical roadmaps, formulate problems, design and productionize large-scale ML systems, oversee model pipelines and system architecture, conduct A/B testing, and manage rollouts. Mentor and develop MLE talent while applying machine learning, statistics, and optimization techniques to real-world risk and trust challenges.

Build and launch scalable distributed systems and AI-powered products for Uber’s consumer incentives business. Own machine learning projects end to end, including scoping, offline evaluation, experimentation, production deployment, algorithm tuning, and post-launch maintenance. Collaborate with product, operations, and data science stakeholders while optimizing consumer experiences and business outcomes across grocery and food verticals.

Build and scale machine learning and backend distributed systems for Uber AI Solutions. Own an AI product vertical end to end, driving technical direction, customer value, revenue, adoption, and market growth. Partner with product and business leaders, guide strategic customer relationships, make decisions in ambiguous environments, and build high-performing teams. The role focuses on production ML, GenAI and LLM systems, evaluation, multimodal data, and other applied AI domains.

Leads the design, development, deployment, and maintenance of production machine learning systems at scale. Responsibilities include building and validating models, optimizing ML infrastructure and data pipelines, automating testing and deployment, monitoring and retraining production models, applying cloud technologies, and ensuring responsible, explainable, secure, and highly available ML applications. Collaborates with Product, Data Science, and Agile engineering teams.

Design, build, deploy, and maintain machine learning models and applications at scale. Develop production-grade code, data pipelines, cloud-based ML architectures, and automated testing and deployment workflows. Monitor and retrain production models, optimize performance, and collaborate with Product, Data Science, and Agile engineering teams. Apply responsible AI, model governance, explainability, and security best practices.

Leads enterprise AI and machine learning strategy, architecture, engineering teams, MLOps, CI/CD, cloud integration, AI governance, and responsible AI practices. Partners with executives, product owners, and architects to build roadmaps, deploy scalable intelligent systems, and deliver measurable value across health financial platforms.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.