Top AI & Machine Learning Jobs

Master’s thesis focused on automating the initial calibration of vehicle cabin air-quality monitoring models using machine learning. The work includes creating datasets from vehicle characteristics and calibration parameters, identifying key influences, developing predictive models, generating initial parameters for new vehicle platforms, and validating predictions against measurement data and conventional calibration. Candidates should have data analysis and machine learning experience, knowledge of thermodynamics and fluid dynamics, and basic MATLAB or Python skills.

Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Test and validate AI models and software for automotive use cases: generate ground truth, build annotated datasets and testing frameworks, evaluate technologies, support R&D engineers, coordinate projects and outsourced work, and collaborate with internal and external partners to drive innovation and product readiness.

Design, develop, deploy, and maintain full-stack agentic AI applications from concept through production. Responsibilities include agent orchestration, data and tool integration, MCP server connectivity, frontend UI/UX development, cloud deployment, engineering-process analysis, technology evaluation, testing, validation, documentation, and innovation project coordination. The role collaborates with R&D teams and internal customers to create reliable AI solutions that improve efficiency, quality, and scalability.

Develops next-generation AI, machine learning, generative AI, and agentic AI solutions for engineering workflows. Responsibilities include researching techniques, evaluating approaches, building and validating production-ready applications, APIs, services, and data pipelines, and transitioning solutions to internal customers. The role also evaluates emerging technologies, supports R&D programs, coordinates innovation projects, analyzes engineering results, and collaborates with engineering teams, universities, suppliers, and strategic partners.

Supports ADAS environmental modeling teams by developing, integrating, testing, tuning, and debugging perception algorithms for parking, trailering, surround view, camera calibration, and sensor fusion. Responsibilities include image processing, deep learning model development, test-case creation, system testing in bench and vehicle environments, troubleshooting, root-cause analysis, issue documentation, and proposing corrections.

Master’s thesis investigating AI-assisted review and verification of automotive electronic hardware designs. Responsibilities include reviewing schematics, identifying errors and inconsistencies, generating verification test cases from requirements and circuit data, improving schematic quality, and evaluating AI-generated results against traditional engineering methods. The project will assess practical applications, limitations, and future integration of AI and large language models into ECAD workflows.

Owns development, customization, validation, and customer approval of automotive camera functions, including object, traffic sign, signal, lane, calibration, and defect detection. Leads requirements management, system analysis, cross-functional coordination, production process support, and field-return investigations. Requires expertise in camera geometry or machine-learning-based computer vision, statistical analysis, numerical algorithm development, digital imaging, and customer-facing technical coordination.

Owns development, customization, validation, and customer approval of automotive camera functions, including object, traffic sign, lane, calibration, and defect detection. Leads requirements management, system analysis, field-return investigations, production-process support, and cross-functional coordination. Requires expertise in camera geometry or machine-learning-based computer vision, numerical algorithm development, digital imaging, statistical analysis, and customer-facing technical communication.

Designs, builds, and operates enterprise AI/ML platforms and production services. Responsibilities include infrastructure-as-code, MLOps and CI/CD pipelines, model deployment, API and data integrations, observability, performance optimization, security, disaster recovery, and model-drift monitoring. The role develops reusable code, implements RAG and agentic workflows, and collaborates with architecture, data science, DevOps, security, and product teams while documenting systems and communicating technical concepts to stakeholders.