Top Machine Learning Engineer Jobs

The Software Engineer will develop infrastructure for ML models, optimizing the economic engine for ride-hailing services and managing backend platforms for real-time decision-making in pricing and vehicle matching.

Develop machine learning models and infrastructure for assessing autonomous vehicle behavior. Collaborate across teams to optimize production systems and model performance.

Develop and enhance the data infrastructure for Waymo's ML models, focusing on data needs, storage efficiency, and integration across various use cases.

Develop ML-first behavior modules for predicting future motions of road users. Collaborate with perception and planning teams to enhance autonomous systems.

Design and implement distributed cloud GPU training for ML, build end-to-end pipelines, collaborate on data problems, and uphold ML best practices.

The ML Perception Software Engineer will develop perception modules for autonomous vehicles, focusing on machine learning and computer vision to enhance navigation capabilities.

Design, operate, and optimize a scalable ML platform: productionize ML workloads, implement governance (Unity Catalog), build CI/CD with Terraform and GitHub Actions, configure IAM/RBAC, establish observability, and enable data scientists to move models from notebook to production.

Responsible for designing, building, and operating AI-powered features, scalable APIs, and maintaining integrations within ClickHouse's AI/ML ecosystem.

The role involves building standardized training frameworks, profiling model performance, collaborating with teams, and improving related tooling.

The role involves designing and implementing MLOps pipelines, optimizing machine learning processes, and ensuring model performance and compliance in a collaborative environment.

Lead the development of AI systems for an automation platform, focusing on design, implementation, and evaluation of AI/ML projects in financial services.

The Senior Machine Learning Engineer will design innovative machine learning models for drug discovery, leveraging large-scale single-cell datasets and collaborating with ML Scientists to develop optimized models.

As a Software Engineer in ML Infrastructure, you will develop scalable data pipelines, enhance data discovery, and collaborate with teams on data organization and ML features.

As an ML Research Engineer, you'll design experiments, implement model architectures, and engineer scalable libraries to advance molecular simulations to support drug discovery.

The ML Research Engineer will develop training pipelines for atomistic simulation models and collaborate with researchers to optimize training workflows.

The role involves architecting molecular machine learning systems, building workflows for data generation and training, and collaborating between research scientists and infrastructure teams.

The role involves building production ML models for voice AI, focusing on fine-tuning, deploying, and evaluating models to enhance human-like interactions.

Design, develop, and deploy production-grade machine learning models and pipelines. Preprocess and analyze large datasets, collaborate with cross-functional teams, monitor model performance, and implement scalable ML solutions to improve customer experiences.

Lead initiatives to enhance the ML platform, mentor junior engineers, build scalable ML models, and ensure code quality while driving innovation in a startup environment.

The Senior Machine Learning Engineer will lead the development of a GenAI platform, enhance AI infrastructure, mentor junior engineers, and drive project ownership in a startup environment.

Design and develop machine learning infrastructure, enhancing core components for model training, monitoring, and serving. Collaborate with ML engineers on robust pipelines.

The Machine Learning Engineer will develop production-grade ML pipelines, optimize research code, and implement cloud infrastructure for ML workloads at Harmonic.

The Senior Machine Learning Engineer will design and deploy ML models, mentor junior engineers, and contribute to project architecture, enhancing security applications.

The AI/Machine Learning Engineer II will design and develop models to analyze data for health monitoring devices, focusing on diagnosis and prediction using various algorithms.

The Sr Engineer will develop AI/ML models for health monitoring devices, focusing on algorithm development, data analysis, and cross-functional collaboration.