Top Machine Learning Jobs

Lead development of AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, ensure responsible AI compliance, produce documentation/patents/publications, and collaborate on MLOps pipelines and stakeholder integrations.

As a Machine Learning Engineer, you'll analyze data to optimize products for the ad platform, enhance ad relevance, and design performance models.

The role involves designing and maintaining machine learning models, analyzing data, and optimizing algorithms to solve business problems.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

The role involves building and improving systems for training AI models, focusing on algorithms and infrastructure for reinforcement learning. Responsibilities include enhancing performance, reliability, and usability of systems to support research.

Lead the design, execution, and analysis of machine learning-driven product experiments to optimize ordering systems. Collaborate with stakeholders and mentor teams.

Develop applications for machine learning and generative AI technologies, automate data analysis, and collaborate on prototyping solutions for semiconductor testing.

Conduct applied research in robotics and develop ML pipelines for training models on data collected by Scale, collaborating with customers and cross-functional teams.

Lead the development of AI/ML systems in robotics and manufacturing, designing ML architectures, and collaborating with cross-functional teams.

As a Machine Learning Engineer, you'll design, build, and deploy ML applications, collaborate with teams, and optimize models and data pipelines. You'll focus on both model development and ensuring high performance in production environments.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

As a VyStar intern, you will collaborate on projects focusing on machine learning, data science, and agile workflows while gaining hands-on experience and mentorship.

As the Area Technical Lead, you will architect and improve the ML data strategy for autonomous driving, enhancing the data-driven self-improvement process for the Waymo Driver.

The intern will research advancements in ML systems, model performance for Lightmatter systems, and collaborate on design for reference platforms, aiming for academic publication.

Design and implement ML models and systems for the Highlight AI desktop assistant, collaborating with cross-functional teams to integrate AI features into the product.

The R&D Intern will develop and evaluate machine learning models, implement data processing pipelines, and collaborate with engineering teams on ML solutions for wireless communication.

As a Senior Machine Learning Engineer, you'll design, develop, and implement scalable ML applications, collaborate with cross-functional teams, and optimize models while ensuring high performance and responsible AI practices.

Work on prototyping and implementing audio ML algorithms for real-time applications. Present findings and integrate solutions into existing platforms. Collaborate with experts in ML and DSP.

Develop and optimize predictive models for credit and risk, leveraging advanced machine learning techniques to improve accuracy and decision quality.

As a Machine Learning Engineer, you'll evaluate neural network quality, coordinate with researchers, manage experiments, and report results to streamline AI software development.

Lead development of end-to-end cybersecurity data science solutions: define problem statements, build and validate ML/AI models, collaborate with engineers and analysts, and manage team members to produce reproducible, production-ready results.

The Senior Machine Learning Platform Engineer will build foundational components for ML models, mentor junior engineers, and optimize streaming pipelines. Responsibilities include enhancing ML infrastructure and ensuring data quality.

Lead production of machine learning applications, provide technical expertise, and mentor engineers to drive ML model delivery at scale.

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.