Top Senior Machine Learning Jobs

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Develop novel AI approaches for healthcare using structured and unstructured data, build ML models, conduct experiments, and collaborate with stakeholders.

Develop AI approaches using structured and unstructured data, build ML models, perform statistical analysis, and collaborate with stakeholders and experts.

As a Machine Learning Engineer in the Buyer Growth team, you'll design and deploy ML models to drive user acquisition and improve retention. You'll manage the entire model lifecycle and collaborate across teams to identify impactful growth opportunities.

Lead research on machine learning validation for robotics and autonomy systems, focusing on simulation, uncertainty modeling, and creating functional prototypes for testing.

Design, build, deploy, and maintain production-scale ML models and pipelines. Collaborate with product and data science teams to architect ML systems, implement model training and monitoring, optimize performance and reliability, and apply Responsible/Explainable AI practices using cloud and distributed computing technologies.

As a Senior Machine Learning Engineer, you'll design scalable AI/ML infrastructure, optimize model training performance, and collaborate with teams to implement advanced AI solutions.

Lead design, training, and evaluation of multi-modal ML perception models (camera, lidar, radar) for fallback autonomy. Define metrics, curate datasets, implement training and optimized inference pipelines for on‑vehicle constraints, and integrate models into production. Partner with safety, systems, and product teams for validation, verification, and deployment. Mentor engineers and promote ML and software engineering best practices.

Develop and deploy machine learning models on the da Vinci surgical robot by writing C++ applications, connecting to robot data streams, integrating model outputs, and using Linux build systems and command-line tools. Gain system-level understanding of the robot and collaborate with ML engineers/scientists. CUDA experience and ML knowledge are beneficial.

Lead end-to-end improvements to Hinge's recommendation system, partnering with data science, ML, engineering, design, and research to personalize ranking, run experiments, protect fairness and trust, define roadmaps, and drive measurable user and business outcomes.

Conduct research and develop identity and probabilistic-matching models for Netflix's ad-supported tier. Apply ML and statistical methods, ensure privacy-preserving solutions (differential privacy, anonymization), prototype and deploy production models, deliver datasets and tools, and partner with product, engineering, and business stakeholders to measure and drive impact.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

Lead development and production deployment of ML and deep-learning models for sponsored ads selection, relevance, ranking, CTR prediction, auction dynamics, and campaign optimization. Collaborate with product and engineering, run experiments, mentor junior scientists, deploy distributed pipelines at scale, communicate results to senior leadership, and publish research in ML/advertising venues.

The Machine Learning Scientist will research, develop, and deploy machine learning solutions for real-time healthcare decision systems focusing on maternal health. Responsibilities include evaluating methodologies, collaborating with teams, and achieving product development goals.

Lead development of scalable analytic solutions for intelligence missions: build production-ready Python tools, automate pipelines, design visualizations and dashboards, apply data modeling and geospatial analysis, and provide technical leadership across multidisciplinary teams to modernize analytic workflows.

As Lead ML Engineer, you will architect and build a conversational AI interface, optimize models for deployment, and collaborate with a multidisciplinary team.

The role involves designing and scaling ML systems for prediction and recommendation, mentoring junior engineers, and leading projects.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

As a Sr. Staff Machine Learning Engineer, you will drive the development of cloud security features, leveraging AI/ML to solve real-world challenges and optimize production systems.

Design, build, and deploy large-scale machine learning models powering Snapchat features. Own full ML lifecycle from data analysis to production, prototype with cross-functional partners, apply modern ML techniques, and use AI tools while ensuring code correctness, security, and scalable system design.

Design, build, and operate production ML models, services, and platforms for pricing, risk, and decision systems. Own end-to-end model pipelines, deployment, monitoring, and iteration. Collaborate with researchers and product to productionize prototypes, tackle data drift and sparsity, lead design reviews, and mentor teammates.

Design and deploy production-grade machine learning models and data pipelines across image, video, text, geospatial, time-series, and structured data. Collaborate with customers, data scientists, software and DevOps engineers to define requirements, maintain mission-critical deployments, and contribute to the Chariot AI operations platform.

Lead enterprise AI architecture for telecom by designing target-state frameworks, building and deploying production ML models for network optimization and predictive maintenance, integrating AI into OSS/BSS, implementing real-time streaming and edge/cloud solutions, establishing model governance and lifecycle management, and mentoring cross-functional teams to align technology with business outcomes.