Top Machine Learning Jobs

Lead the delivery of ML initiatives, define roadmaps with cross-functional teams, and develop ML models for conversational agents and smart tools, enhancing customer support.

As a Senior Machine Learning Engineer in Model Risk Management, you will evaluate and validate models that assess risk in lending and fraud, ensuring their robustness and reliability. You will collaborate with model owners, tackle model challenges, and build evaluation tools critical for understanding model performance.

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.

Develop and maintain AI/ML applications, focusing on agent-based workflows, API integration, and enterprise deployment, requiring senior-level experience.

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

Responsible for managing the AI/ML project lifecycle, developing low-latency ad-serving platforms, and enhancing search solutions through advanced modeling techniques.

This role involves developing AI/ML architectures, resolving complex issues, and partnering with stakeholders to enhance business outcomes through technology and data.

Design and guide enterprise-scale, cloud-native solutions leveraging software engineering and AI. Provide technical leadership across SDLC, design mobile/cloud apps, integrate via APIs and data services, implement CI/CD and release/configuration practices, and architect end-to-end ML pipelines for cloud and edge deployment while aligning solutions with business objectives.

As a Machine Learning Engineer, you will own the execution layer for ML projects, including data preprocessing, training, benchmarking, and collaboration with researchers to deliver actionable results.

Develop and deploy state-of-the-art physical AI for outdoor robotics, focusing on perception, planning, control, simulation-driven training, multi-sensor integration, data pipelines, and GPU-optimized training and inference.

Lead research and development of end-to-end ML models for autonomous driving, experiment with large-scale datasets and architectures, collaborate with infra and autonomy teams, optimize models for deployment, and deploy solutions to on-road vehicles.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Develop and evaluate ML and sensor-fusion algorithms (vision and radar) for detection, tracking, and classification of aircraft and hazards. Integrate ground and airborne sensors via IoT, run experiments at NASA centers, conduct literature reviews, support ML challenges, and publish/present research.

As an Applied Scientist II, you will work with cross-functional teams on projects that optimize software systems and improve ML components, while analyzing inefficiencies and proposing solutions.

Conduct transportation safety research and analysis to inform Vision Zero and Bus Action Plan; maintain and mine datasets; build predictive machine learning models and data products; perform ad-hoc and geospatial analyses; prepare visualizations and present findings; supervise collaborators and support agency data-driven decision-making.

Design, develop, and deploy end-to-end machine learning and large language model solutions. Perform data discovery, cleaning, preprocessing, and visualization. Build/train LLMs and Agent+RAG systems, collaborate with stakeholders and AI teams, and apply graph-based analysis to support enterprise integration and analytics.

The role involves conducting research and optimizing machine learning systems while collaborating with technical staff to enhance product performance.

Lead the sports-content product owning real-time low-latency data pipelines, proprietary ML models, fan LTV and identity, rights compliance, AI-driven highlights, sponsorship analytics, and senior client/commercial relationships to deliver sub-500ms actionable intelligence for betting, broadcast, and rights customers.

As a Senior Machine Learning Engineer, you will develop advanced machine learning and deep learning models and platforms for optimizing advertising performance and conduct complex experiments.

As a Reinforcement Learning Engineer, you will own production trading systems, design reward functions, validate frameworks, and lead RL efforts to drive trading volume safely with real capital.

Develop and optimize lightweight machine learning models for edge AI applications, integrating into production systems and mentoring junior engineers.

The Senior AI Scientist will develop innovative machine learning techniques for MRI, coordinating with teams and enhancing product capabilities through advanced model development and software maintenance.

Design, implement, and deploy machine learning algorithms and scalable ML solutions; collaborate with data scientists, engineers, and federal customers to build analytics capabilities and features; support application deployment and customer enablement.

The Machine Learning Engineer will develop and improve ML models for product surfaces, build data processing pipelines, and leverage machine learning technologies in advertisements.

Lead development of motion planning and prediction ML models for Level 4 autonomous vehicles. Design and productionize behavior models using deep learning, generative AI, and reinforcement learning. Deploy solutions to vehicle fleets, collaborate with perception, simulation, data platform and integration teams, and report progress to leadership.