Top Python Jobs

The Senior Software Engineer will design, develop, and maintain software for defense systems, leading a team while ensuring client needs and system requirements are met.

As a Staff Software Engineer, you will build technical foundations for self-driving technology, ensuring safe and efficient delivery methods while collaborating within a team.

As a Software Test Engineer, you will perform testing and quality assurance for IT systems, collaborate with development teams, and assess systems against technical requirements.

Develop backend systems for self-driving technology, manage large datasets, and collaborate across teams to improve ML model evaluations.

The Integrations Developer will architect and implement scalable integrations, automate processes, ensure data quality, and collaborate with teams to enhance operational efficiency.

Lead technical initiatives to improve infrastructure and engineering workflows while collaborating on scalable systems; implement architectural enhancements.

The Staff Failure Analysis and Test Engineer analyzes lidar components, improves product quality, and manages testing and validation processes with engineering teams.

Drive MLOps best practices, develop automation in ML lifecycle, and partner with data teams for model deployment and monitoring.

As a Senior Research Engineer, you'll prototype machine learning methods, engage with the scientific community, and guide junior researchers while working on advanced digital media technology.

Design and implement scalable systems for geospatial data; deploy machine learning models; ensure system performance in cloud environments; collaborate in a dynamic team.

The Senior Research Engineer will prototype machine learning approaches for game content creation, deploy technologies, mentor junior staff, and contribute to research efforts.

The Azure Administrator will oversee Azure deployments, provide technical expertise, guide junior engineers, and ensure proper cloud architecture and security settings are implemented.

The Agentic AI Engineer will create scalable ML systems, deploy and monitor solutions, and build reusable data pipelines while collaborating with teams to drive strategy and ensure business value.

The role involves developing ML models and data pipelines, collaborating with various teams, and delivering data-driven solutions for digital health.

The Cloud Data Architect leads cloud data architecture design, mentors Data Engineers, and collaborates with clients on data solutions and analytics frameworks.

The Machine Learning Engineer will design, develop, and deploy scalable ML models using Google Cloud Platform and Vertex AI, ensuring performance optimization and API development.

The Gen AI Data Engineer will design and build distributed data systems, manage data pipelines, and ensure data integrity across platforms. Responsibilities include architecting data solutions, integrating databases, and conducting experiments to optimize performance.

The Data Engineer will build and manage scalable data pipelines on AWS, optimize data solutions, and collaborate with cross-functional teams.

Design and transform code bases into scalable cloud infrastructure. Collaborate with teams to enhance efficiency while managing GCP use and guiding engineers on best practices.

The Senior Controls Engineer will design and implement control software for autonomous rail vehicles, focusing on real-time systems integration and performance.

As a Product Engineer, you'll build scalable enterprise applications, collaborate with teams, and leverage advanced analytics in data engineering.

Lead data security analysis and design Databricks access control mechanisms while collaborating with architects and documenting implementations.

The ML Infrastructure Engineer will develop and manage ML infrastructure for autonomous vehicles, optimizing data pipelines, and enhancing model deployment processes.

Design and develop mechanical systems for autonomous vehicles, focusing on integration with software and testing designs through simulations and real-world applications.

Develop and implement deep learning models for autonomy applications, improve ML algorithms, handle datasets, and solve real-world issues in deployment.