Top Machine Learning Engineer Jobs

The role involves designing ML pipelines for computer vision, optimizing models for edge deployment, and developing data management systems for sensor datasets.

The Senior Software Engineer will develop and optimize algorithms and ML models for mobile, embedded, and cloud environments. Responsibilities include collaborating with cross-functional teams, deploying algorithms, analyzing performance, and ensuring code quality.

Develop algorithms in machine learning and statistics to address complex challenges. Design data pipelines and deploy solutions using cloud infrastructure.

The Software Engineer will build components for Red Hat's AI platform using Python, implement features and collaborate with senior engineers, while participating in agile ceremonies.

The Senior Machine Learning Engineer will lead applied research projects, design and implement ML models, maintain data pipelines, and mentor junior members for defense-focused missions.

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

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.

The Data Engineer will modernize Business Intelligence capabilities, design data pipelines, deploy ML solutions, and collaborate across teams to improve data processes.

The Senior Machine Learning Engineer will design, train, and evaluate ML models to solve complex problems, mentor others, and optimize production systems.

The Research Engineer will analyze AI systems, develop AI training platforms, collaborate with teams, and enhance educational applications using generative AI.

As a ML Ops Engineer, you'll develop a centralized data infrastructure, build multi-tenant architecture, manage asynchronous job orchestration, and enhance machine learning pipelines for building upgrades.

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

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.

As the ML Ops Engineer at Tennr, you will architect and implement ML systems, develop infrastructure for ML operations, and collaborate with teams to enhance model integration and performance.

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.

As a Lead Machine Learning Engineer, you will develop and implement ML models, optimize data pipelines, and collaborate with cross-functional teams to enhance business solutions.

As a Senior ML Ops Engineer, partner with teams to build DevOps solutions, automate processes, integrate infrastructure, and drive quality improvements to enhance products and services.

The role involves building systems for large-scale model training, focusing on distributed training, ML infrastructure, and GPU performance optimization.

The Software Engineer II will develop scalable systems for ML, manage data pipelines, collaborate with scientists, and optimize GPU workloads.

The Senior Machine Learning Engineer will design, develop, and deploy production ML models, owning the entire ML lifecycle and collaborating with various teams to enhance product experiences and decision-making systems.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

As an Applied Machine Learning Platform Engineer, you'll design and maintain training infrastructure, manage distributed pipelines, and optimize data workflows for machine learning models.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.