Expert Perception Data Engineer (m/f/d) at driveblocks GmbH

Munich, Bavaria, Germany -

Full Time

Start Date

Immediate

Expiry Date

20 Aug, 26

Salary

0.0

Posted On

22 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, C++, Data Pipeline Design, Multimodal Sensor Data, Dataset Curation, Scenario Mining, Embeddings, Vector Databases, Semantic Search, Dataset Versioning, Cloud-native Workflows, Distributed Compute, Machine Learning, Robotics, Metadata Management, Quality Assurance

Industry

Software Development

Description

Your Role As an Expert Perception Data Engineer you will scale the data workflows that power the next-generation of off-road autonomy systems. You own the full data lifecycle from ingestion and curation to automated scenario mining, dataset generation, embeddings-based retrieval, and validation workflows. In this hands-on engineering role, your designs and implementations have a critical impact on enabling the robust and safe performance of our software in challenging off-road conditions. Your main tasks are: Designing and scaling large-scale perception data pipelines for terabytes of incoming multimodal sensor data. Developing automated dataset curation and scenario mining systems for edge cases and safety-critical situations. Building embeddings-based retrieval and semantic search pipelines for perception data selection and analysis. Maintain tooling for dataset versioning, metadata management, annotation orchestration, and quality assurance to meet certification requirements. Improving infrastructure for high-throughput data processing, cloud-native workflows, and distributed compute pipelines. Driving automation around data operations, labeling processes, and deployment feedback loops from field operations. Your Background 6+ years of professional experience building large-scale data processing systems for machine learning or robotics applications Strong software engineering skills in Python and/or C++ Experience working with multimodal sensor data such as camera, lidar, and radar Familiarity with dataset management tooling, embeddings, vector databases, semantic retrieval, and similarity search systems Strong ownership mindset and the ability to work independently in a fast-moving engineering environment Strong communication skills and the ability to explain complex technical topics clearly across engineering and customer-facing teams Degree in computer science, robotics, engineering or a related field(Bachelor, Master, or PhD) We understand that not everyone will meet all the criteria! If you see yourself in these points, we’d love to learn more about you! About Us driveblocks develops off-road ground autonomy that works under harsh and challenging conditions. We build systems designed for operation in 3D terrain, vegetation, and heavy dust and dirt, where 100% reliability is core to success, even in GNSS-denied environments. Working with multiple OEM partners across agriculture, construction, and defense, our focus is to deliver value to vehicle operators and solve the toughest challenges when deploying Physical AI in real world applications. Every deployment helps us to gather the operational data required to build the next generation of reliable and safe autonomous driving systems. From our office in Munich, we operate as a focused, highly ambitious team with deep expertise in autonomy, Physical AI, and embedded software engineering. We move quickly and set high standards. If you want to build autonomy that is deployed, tested, and operated in the field - we’d love to hear from you! Working at driveblocks Ownership of your work – Take responsibility for the full value chain Competitive Compensation & Benefits Participate in driveblocks’ success via a Virtual Stock Option Plan Flexible working hours How To Apply Upload your CV and additional documents Show your unique motivation

Responsibilities

Scale data workflows for off-road autonomy systems, managing the full lifecycle from ingestion to validation. Design large-scale perception pipelines and automated curation systems for multimodal sensor data.