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MACHINE LEARNING ENGINEER – AUTOMOTIVE SOFTWARE

Lisboa - Lisboa

Descrição da oferta de emprego

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives.
Our promise to our associates is rock-solid.
we grow together, we enjoy our work, and we inspire each other.
Join in and feel the difference.
With over 300 specialized associates and around 15 nationalities, Bosch activities in Lisbon are focusing on sales, marketing, communication, training, nearshoring services and shared support for human resources services to Europe.
The diversity, commitment and know-how of the team are the key success factors of this organization.
Bosch Service Solutions develops and delivers Customer Experience, Mobility and Monitoring solutions for companies and their customers.
Our teams find fast and efficient solutions for thousands of people on a daily basis – from claim management to life-saving emergency calls, among other services.
Define the standards and become a pioneer together with around associates worldwide.
Job Description We are looking for a highly skilled and motivated Machine Learning Engineer to join our team.
As part of our AI and machine learning group, you will develop state-of-the-art models that enable intelligent decision-making related to vehicle exterior and interior perception based on IMUs, microphones and air sensors.
You will work on projects that require sensor fusion and real-time sensor data processing, ensuring our solutions are robust, scalable, and optimized for automotive-grade performance.
Design, develop, and deploy machine learning models for ADAS and real-time monitoring  in vehicles.
Implement algorithms for   environment perception using data from IMUs, cameras, and radar.
Develop sensor fusion techniques to integrate data from multiple sensors and improve overall vehicle perception and safety.
Collaborate with software and hardware teams to optimize machine learning algorithms for real-time, embedded automotive systems.
Conduct extensive testing and validation of models using simulation tools and real-world datasets under various driving scenarios.
Work on continuous improvement of deployed models by processing large datasets and applying advanced machine learning techniques Stay updated with the latest advancements in AI, machine learning, and automotive technologies, incorporating new methods and tools into our products.
Qualifications Bachelor’s or master’s degree in computer science, Electrical Engineering, or a related field (PhD is a plus).
3+ years of hands-on experience in developing machine learning models for real-time systems, preferably in automotive or related industries.
Strong proficiency in Python and C++ with experience using machine learning frameworks (e.
., TensorFlow, PyTorch, OpenCV).
Familiarity with sensor technologies such as inertial sensors, LIDAR, radar, cameras, and the integration of their data.
Knowledge of ADAS and embedded systems for real-time machine learning.
Experience with optimization techniques for deploying machine learning models on resource-constrained devices.
Experience in algorithm development on perception features for ADAS systems.
Familiarity with edge computing and deploying AI solutions on automotive-grade hardware.
Previous work with large-scale automotive datasets and sensor data processing.
Ability to work in a fast-paced, collaborative team environment.
Strong problem-solving and analytical skills, with attention to detail.
Excellent communication skills, capable of explaining complex technical concepts to non-technical team members.
Additional Information What we offer you.
Challenges in an international environment | career opportunities in one of the best companies in the world | flexible hours | medical services | discounts for employees | sports and health related activities | canteen | good access to public transports | space for creativity.
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Detalhes da oferta

Empresa
  • Indeterminado
Localidade
Endereço
  • Indeterminado - Indeterminado
Data de publicação
  • 14/10/2024
Data de expiração
  • 12/01/2025
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