Top Machine Learning Engineer Jobs

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.

Develop advanced Vision-Language-Action models for robotaxis, leading data strategies and improving driving behaviors using machine learning infrastructure.

Develop and deploy machine learning models for national security challenges. Responsibilities include algorithm development, model training, deployment, and collaboration with teams for effective ML solutions.

The Senior Principal Engineer will lead the development of ML systems at SoundCloud, focusing on recommendation and personalization techniques while mentoring engineers and driving strategic direction.

Lead the design and implementation of scalable AI systems focusing on large language models and speech technologies, while providing technical leadership and mentoring.

As a Machine Learning Engineer, you'll build and enhance features for a search stack, design ML/NLP solutions, and implement an experimentation framework while ensuring code quality.

The ML Engineer will design, develop, and implement machine learning models, manage ML pipelines, integrate APIs, and collaborate with teams to deploy solutions.

The Founding ML Engineer will develop and deploy ML systems, collaborate with teams, and translate ambiguous problems into production ML solutions, shifting between client delivery and platform development.

The Principal AI/Machine Learning Engineer will lead the adoption of AI/ML in manufacturing by defining use cases, driving implementation, and ensuring organizational readiness.

The Machine Learning Engineer will conduct data analysis, develop ML applications, manage data transformations, and support API and ETL implementations.

Design and manage evaluation datasets, develop evaluation pipelines, score assistant quality using LLMs, and build observability for AI agents.

The role involves designing, implementing, and optimizing CUDA-based kernels for autonomous vehicles, ensuring high-performance ML inference for AV software.

Lead end-to-end ML projects for automotive manufacturing: design, train, validate, deploy, and monitor models; implement MLOps/CI-CD; perform data engineering, EDA, and model evaluation; ensure data security and regulatory compliance; document and communicate results to stakeholders.

This role involves developing ML tools and frameworks, supporting data science environments, and leading projects from design to execution. Candidates need strong programming skills and a quantitative background.

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

The Senior Staff Machine Learning Engineer will improve Coupang's search and discovery systems using advanced algorithms, addressing bottlenecks, proposing solutions, and leading projects involving personalization and recommendations.

The Applied ML Engineer will research and build AI/ML systems, own model development, partner with teams to impact marketplace metrics, and establish best practices for AI/ML at Paraform.

Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking in maritime intelligence.

Lead the design and production of ML systems for Pinterest's ads measurement products. Collaborate with various teams to improve measurement quality and contribute to technical direction and best practices.

Design and implement ML algorithms for bidding and ranking systems in ads. Own complete ML systems from conceptualization to production. Collaborate with teams to integrate models and enhance advertising value using AI.

Design and implement AI agents for mission planning and orchestration across autonomous systems, focusing on LLM frameworks and edge computing.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

The role focuses on optimizing and deploying generative AI models in production environments, working directly with customers to ensure optimal performance and integration solutions.

As a Machine Learning Engineer Intern, you'll tackle challenges in ML including training models, optimizing inference on GPU, and documenting findings.

The role involves training and fine-tuning vision and language models, building evaluation and learning pipelines, and optimizing model performance.