Localização
Munich, Alemanha
Modalidade
Presencial
Contrato
Tempo inteiro
Senioridade
Lead
Categoria
TI
Categoria IT
Engenharia de dados
Idioma
English
Publicado
28 de Maio de 2026
Última verificação
28 de Maio de 2026
Contexto da JobGrid
Resumo da vaga pela JobGrid
Senior Data Engineer (F/M/D) at AlphaIgnis: Munich, Alemanha; Presencial; Tempo inteiro; Lead; TI. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Munich, Alemanha, Presencial
- Role classification: TI, Engenharia de dados, Tempo inteiro, Lead
- Source freshness: checked by JobGrid on 2026-05-28.
- Application path: candidates continue to the employer application page with non-personal referral tags.
The Opportunity
We’re looking for a Senior Data Engineer to architect and scale the data backbone powering next-generation AI models in robotics and real-world environments.
This role sits at the intersection of distributed systems, multimodal data processing, and applied machine learning, with a strong focus on building high-quality datasets for robotic foundation models. You will ensure that data pipelines, infrastructure, and data strategy directly translate into measurable improvements in model performance.
Your Responsibilities
- Drive the model–data loop by connecting application requirements with data collection, and translating model failures into data-driven improvements through collection, curation, and augmentation
- Build and scale distributed data pipelines (Ray/Anyscale or similar) for TB-scale video, sensor, and robotics datasets
- Design multimodal data schemas aligning video, actions, and high-frequency sensor streams
- Develop Python tooling for data quality, including cleaning, anomaly detection, and dataset versioning
- Own dataset quality and coverage, including annotation workflows, data diversity, and storage trade-offs
- Lead a small team and coordinate with data providers and annotation vendors
- Oversee real-world data collection, including technical setup, compliance, and secure data handling
Technologies
- Python (advanced, production-grade)
- Ray / Anyscale or Apache Spark
- AWS / GCP for large-scale data and GPU training pipelines
- Video and sensor data formats (H.264/H.265, ROS bags, MCAP)
- PyTorch, NumPy
- DVC, LakeFS or similar data versioning tools
- Distributed data processing and storage systems