Top Machine Learning Engineer Jobs

Lead the design and deployment of scalable machine learning systems, focusing on Generative AI and NLP solutions, while mentoring junior engineers.

The Senior AI/ML Platform Engineer will build AI/ML tools and services, contribute to platform design, and ensure usability and performance at scale, while collaborating closely with various teams.

The Software Engineer Manager, Machine Learning will lead a team, drive machine learning projects, support engineering talent development, and ensure collaboration with product teams.

The Sr. Engineer, Machine Learning Operations designs and operates ML solutions for cancer screening. They build robust pipelines and collaborate with cross-functional teams.

As a Staff Software Engineer on the Machine Learning team, you'll build scalable systems, integrate ML models, and oversee code quality and project mentorship.

The Senior Engineer in Machine Learning will develop algorithms using machine learning techniques on biomedical datasets, prototype solutions, and collaborate with teams to ensure data quality and performance measures.

Responsible for designing and implementing scalable ML software solutions for Search & Discovery, processing massive datasets, and optimizing ML pipelines for real-time applications.

Lead architecture, development, deployment, and monitoring of generative AI applications. Build ML pipelines, model optimization (LoRA/QLoRA), guardrails, monitoring, and onboarding frameworks. Collaborate with product and data teams, mentor engineers, ensure production reliability, and drive feature development.

The Senior Machine Learning Engineer will develop and implement ML algorithms for robotic manipulation, mentor engineers, and optimize models for real-time performance on robotic hardware.

Develop and maintain advanced perception models for autonomous robots, build cloud infrastructure, and collaborate with teams to enhance ML solutions.

The Senior ML Ops Engineer will architect and implement software solutions, focusing on MLOps, system design, and robust AI applications.

Design and scale the infrastructure for ML systems, enabling reliable trading system production, optimizing performance and ensuring correctness across complex environments.

Design, build, and run large-scale backend and data infrastructure; productionize and run experiments on AI/ML models; implement MLOps best practices (versioning, monitoring, alerting); improve model accuracy, speed, and cost; and drive engineering process and culture improvements.

The role involves productionizing AI models and optimizing systems for secure, distributed environments, focusing on cloud, edge, and mobile applications.

As a Senior Data Science Engineer, you will develop machine learning algorithms and statistical models, perform data analysis, and build data-driven products to enhance real estate solutions.

The Senior Machine Learning Engineer will enhance fraud detection capabilities through ML technologies, improve model performance, and lead high-impact ML projects from experimentation to production.

Develop and deliver AI-driven observability features, collaborate with cross-functional teams, and iterate rapidly to enhance user incident response capabilities.

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.

The ML Systems Engineer will optimize distributed training systems, develop high-performance GPU kernels, and ensure efficient data pipelines for machine learning models.

The role involves optimizing training systems for machine learning, improving performance, and developing high-performance GPU kernels for distributed training.

The role involves optimizing large-scale model training systems focusing on performance, distributed training, and data pipeline engineering using ML and high-performance systems engineering.

Develop, enhance, and maintain applications using Python and SQL within an AI/ML pipeline while leading infrastructure changes and supporting deployments.

Responsible for designing, building, deploying, and maintaining production-grade AI and ML solutions in a cloud-native environment, while collaborating with engineering and data teams.

The role involves designing and developing machine learning infrastructure for annotation, evaluation, and training models, focusing on scalable systems and efficient data workflows.

Responsibilities include designing workflows, optimizing algorithms, developing Machine Learning solutions for EDA, and collaborating with cross-functional teams.