Top Machine Learning Jobs

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 test AI software using large datasets, algorithms, and statistical analysis for industrial applications. Collaborate with machine learning and data engineers to build integrated AI/ML solutions, evaluate performance, monitor industry trends and regulations, and present progress to leadership and stakeholders. The internship offers hands-on experience, mentorship, and exposure to practical AI innovation.

Develop and test AI software for industrial applications using Python, JavaScript, and C++. Evaluate data, statistics, and algorithms; collaborate with machine learning and data engineers; support end-to-end AI/ML integration; monitor industry trends and regulations; and present progress to leadership and stakeholders.

Owns end-to-end development of datasets, reinforcement learning environments, and evaluations for coding agents and software engineering models. Leads engineers, researchers, QA professionals, and data creators; establishes quality processes, pipelines, validation, and review workflows. Collaborates directly with frontier AI labs, fine-tunes models, builds benchmarks, develops automation and internal tools, and supports customer-facing enablement. The role combines hands-on technical leadership, engineering management, operational execution, research, and industry thought leadership.

Leads the design, development, integration, verification, and flight-test delivery of machine learning and autonomy capabilities for uncrewed aircraft. Responsibilities include perception, tracking, sensor fusion, planning, mission execution, model deployment, real-time C++ and Python software, simulation, testing, troubleshooting, and cross-functional system integration. Provides technical leadership and mentorship without direct personnel management. Requires on-site aircraft integration and field-test support in Bingen, Washington.

Design and build scalable ML infrastructure and backend systems for high-impact marketplace applications. Lead architecture, solve low-latency and high-correctness problems, develop and adopt engineering frameworks, and create production-ready technical roadmaps. Partner with Product, Science, and Operations while mentoring engineers and improving engineering standards. The role requires experience with ML models, foundational models or LLMs, MLOps, Kubernetes, data pipelines, distributed workflows, and large-scale scheduling.

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.

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.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.

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 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.

Designs, builds, and operates production machine learning systems at scale. Develops deep learning models for sequential, multimodal, and spatio-temporal data supporting risk assessment, engagement, and event detection. Partners with research, product, and platform teams to deploy reliable ML products across cloud and edge environments. Responsibilities include software engineering, AWS-based deployment, complex SQL, large-scale data processing, model orchestration, and cross-functional delivery of measurable mobility and road-safety impact.

Lead the architecture, development, and deployment of scalable machine learning systems, including the AI/ML integration connecting Xometry with partner manufacturing platforms. Build real-time ML serving, data pipelines, MLOps, governance, monitoring, and observability. Drive technical roadmaps, optimize latency and throughput, ensure software quality and security, collaborate across engineering and product teams, and mentor engineers while delivering production AI capabilities.

Lead the architecture and delivery of scalable machine learning systems, including real-time model serving, MLOps, monitoring, governance, and automated deployment. Own the AI/ML integration connecting Xometry with partner design and manufacturing platforms. Develop cloud-based production systems, optimize model latency and throughput, establish technical standards, mentor engineers, and collaborate with engineering, product, data science, and business teams.

Technical leader responsible for major cross-functional ML and AI initiatives, including long-term technical roadmaps, scalable production systems, generative AI, computer vision, and 2D/3D modeling. The role drives business impact through revenue growth, cost reduction, efficiency improvements, and competitive differentiation. Responsibilities include architecture, MLOps, model governance, infrastructure decisions, stakeholder influence, team mentorship, and translating emerging technologies into manufacturing applications.

Lead the strategy and development of next-generation post-training systems for Waymo’s autonomous driving AI. Advance post-training techniques, research applications for real-world autonomous driving, build scalable metrics, data, and flywheels, and collaborate with ML platform and onboard software teams. The role also requires influencing engineering strategy and stakeholders across the organization, including director and VP-level leaders.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

As a Lead Machine Learning Engineer, you'll manage AI/ML projects, optimize models, enhance ad-serving platforms, and mentor junior engineers.

Lead requirements and systems engineering for an enterprise application integrating AI capabilities. Work with customers, developers, systems engineers, and domain experts to define requirements, create technical specifications and Jira stories, validate features, evaluate AI architectures and model performance, and coordinate AI solution delivery through Agile development. The role requires hands-on technical execution, functional testing, stakeholder communication, and a current Top Secret/SCI clearance with favorable polygraph.