Top Machine Learning Engineer Jobs

As a Staff Machine Learning Engineer, lead teams in designing AI solutions, managing projects, and interfacing with clients while ensuring technical excellence and operational effectiveness.

Scale and optimize a generative conversation engine for enterprise use: build product infrastructure and APIs, optimize dialog performance and memory, enable multilingual support, implement logging/tracing/metrics and debugging tools, and collaborate with ML, product, and support teams to deliver robust, scalable conversational AI.

Lead design and implementation of agentic, conversational AI features for enterprise use cases. Collaborate with ML experts, PM, UX, and GTM to scope, deliver, and scale product-grade LLM/GAI solutions and expand platform reach.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Develops, trains, deploys, and maintains deep learning models and ML pipelines for electro-optical and infrared image processing. Responsibilities include data ingestion, feature engineering, model validation, inference, rapid prototyping, troubleshooting, system maintenance, and evaluating emerging ML tools. The role collaborates with AI, machine learning, and computer vision engineers supporting space-based defense and intelligence applications.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Own machine learning problems end to end, including data, modeling, evaluation, deployment, monitoring, and production optimization. Build training, inference, evaluation, datasets, and benchmarking pipelines for indoor positioning and item localization. Collaborate with applied scientists, engineering, and product teams to deliver enterprise features while optimizing accuracy, coverage, latency, and cost. Contribute reusable infrastructure, architecture decisions, and high engineering standards.

Lead the architecture, development, and deployment of scalable AI/ML and generative AI platforms powering agentic workflows. Own technical strategy, platform evaluations, roadmaps, system reliability, and cross-functional delivery. Mentor engineers and technical leads while establishing enterprise AI best practices. Build multi-component systems involving search, knowledge management, orchestration, databases, streaming, distributed processing, cloud infrastructure, and LLM-based conversational experiences.

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 autonomy algorithms and foundation models for semantic understanding of real-world driving data. Architect scalable machine learning systems, optimize sensor datasets through processing and auto-labeling, and deploy ML techniques into production. The role requires collaboration across platform, product, security, and engineering teams while solving complex autonomous-driving edge cases and maintaining high technical standards.

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.

Lead the technical strategy and architecture for foundation models powering search, recommendations, personalization, and conversational AI across Uber’s Mobility and Delivery products. Own cross-team initiatives involving retrieval, ranking, embeddings, and LLM assistants; guide build-versus-buy decisions; influence product strategy; deliver large-scale ML systems; and mentor senior engineers while connecting model improvements to business outcomes.

Develop machine learning algorithms and foundation models for autonomous driving data, including semantic understanding, edge-case detection, causal modeling, scalable ML systems, sensor-data processing, dataset optimization, and auto-labeling. Collaborate with platform, product, and security engineering teams to deploy ML solutions into production, including safety-critical physical systems.

Develop machine learning algorithms and foundation models for autonomous vehicle data, including scene understanding, causal modeling, dataset optimization, sensor-data processing, and auto-labeling. Design scalable ML systems and collaborate with platform, product, and security engineering teams to deploy models into production, including safety-critical physical systems.

Defines the multi-year technical vision and architecture for foundation models supporting search, recommendations, personalization, and conversational AI. Leads high-impact ML platform initiatives across Mobility and Delivery, drives retrieval, ranking, and LLM system design, and makes build-versus-partner investment decisions. Provides principal-level technical leadership, mentors senior engineers, aligns executives across organizations, and connects model improvements to measurable business outcomes at global scale.

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.

Architect, productionize, and scale autonomous generative AI support agents. Lead agentic planning, memory, retrieval, reasoning, policy enforcement, safety, evaluation, multilingual capabilities, and cost optimization. Partner with Product, Design, and Operations to deliver reliable customer-facing automation at global scale. Mentor multiple engineering pods, establish technical strategy, and guide experimentation, rollout, and reliability standards.

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.

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.

Design, build, operate, and maintain enterprise AI platform services and agent runtimes. Develop APIs, SDKs, MCP servers, tool interfaces, retrieval and embedding pipelines, model serving, evaluation infrastructure, and distributed data foundations. Establish engineering, security, testing, CI/CD, and infrastructure standards while optimizing platform cost and scale. Apply AI/ML capabilities to healthcare workflows, ensure PHI protection and tenant isolation, evaluate emerging technologies, and mentor engineers while influencing technical strategy.

Develop machine learning products and generative pipelines for image, video, language, and audio generation. Build on-device generative ML and augmented reality experiences using diffusion models, collaborate with cross-functional teams to prototype products, and solve open-ended technical problems. The role requires strong Python or C++ development, deep learning framework expertise, and research or engineering experience in generative or efficient models.

Designs and implements production machine-learning workflows for semantic search, classification, entity resolution, retrieval, embeddings, and RAG. Builds reliable AI services, integrates models with data and backend systems, evaluates mission performance, and supports deployment, monitoring, versioning, and MLOps practices. The role also contributes to architecture decisions and may support defense, intelligence, or mission-focused environments.

Designs and implements AI and machine-learning capabilities for cyber and intelligence workflows. The role captures mission-user processes, develops LLM and agentic automations, integrates AI with APIs and enterprise data, and deploys reliable solutions in classified environments. Collaboration with analysts, operators, developers, and leadership is required. Responsibilities include workflow mapping, orchestration, production integration, security governance, and support for rapid national-security decision-making.

Builds and maintains full-stack software products and backend services with a strong Java and Spring Boot focus. Develops APIs, UI, infrastructure, CI/CD pipelines, and production machine learning or GenAI systems. Practices test-driven development, supports incident response, addresses security and compliance findings, performs root-cause analysis, and coaches junior engineers. Collaborates on architecture, participates in Agile ceremonies, and improves observability, deployment readiness, and product quality.