Intrinsic
Senior Research Scientist, Robotic Foundation Models(m/w/x)
Full-timeOn-siteSenior
München
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INIntrinsic
Senior Software Engineer, ML Ops & Infrastructure(m/w/x)
München
Full-timeOn-siteSenior
AI/ML
Nejo AI Summary
Optimizing data pipelines for 1000+ GPU deep learning jobs in industrial robotics. 2 years MLOps or ML infrastructure experience with Python/C++ and Docker/Kubernetes required. Open-source model development and community engagement.
Requirements
- Bachelor's degree in Computer Science, Robotics, Machine Learning, or related field
- 2 years of experience in software development with focus on MLOps or machine learning infrastructure
- Proficiency in programming with Python and C++
- Experience with containerization and orchestration technologies, specifically Docker and Kubernetes
- Experience with deep learning frameworks such as TensorFlow, JAX, or PyTorch
- Experience with cloud computing platforms like Google Cloud Platform
- Basic Front-end experience
- Master’s degree or PhD in Computer Science, Robotics, or related field
- Comprehensive knowledge of the entire image processing workflow
- Experience with MLOps toolkits such as Kubeflow
- Hands on experience with CUDA optimization or deep learning optimization
- Experience deploying machine learning models at scale in production environments
- Familiarity with robotics systems or industrial automation hardware
- Practical experience debugging distributed systems and optimizing network performance
- Experience with accelerator orchestration (e.g., XManager)
Tasks
- Design and implement scalable infrastructure for deep learning models
- Optimize data loading and training speed for over 1000 GPU jobs
- Build data pipelines for distributed computing of robotics data
- Develop APIs and tools for integrating machine learning techniques
- Lead efforts to open source models and engage with the community
- Optimize compute resource allocation to reduce costs and latency
- Create orchestration workflows for running jobs on GKE
- Develop tools for model understanding and analysis
Work Experience
- 2 years
Education
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- C++
- Docker
- Kubernetes
- TensorFlow
- JAX
- PyTorch
- Google Cloud Platform
- Kubeflow
- CUDA
- XManager
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Intrinsic and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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INIntrinsic
Senior Software Engineer, ML Ops & Infrastructure(m/w/x)
München
Full-timeOn-siteSenior
AI/ML
Nejo AI Summary
Optimizing data pipelines for 1000+ GPU deep learning jobs in industrial robotics. 2 years MLOps or ML infrastructure experience with Python/C++ and Docker/Kubernetes required. Open-source model development and community engagement.
Requirements
- Bachelor's degree in Computer Science, Robotics, Machine Learning, or related field
- 2 years of experience in software development with focus on MLOps or machine learning infrastructure
- Proficiency in programming with Python and C++
- Experience with containerization and orchestration technologies, specifically Docker and Kubernetes
- Experience with deep learning frameworks such as TensorFlow, JAX, or PyTorch
- Experience with cloud computing platforms like Google Cloud Platform
- Basic Front-end experience
- Master’s degree or PhD in Computer Science, Robotics, or related field
- Comprehensive knowledge of the entire image processing workflow
- Experience with MLOps toolkits such as Kubeflow
- Hands on experience with CUDA optimization or deep learning optimization
- Experience deploying machine learning models at scale in production environments
- Familiarity with robotics systems or industrial automation hardware
- Practical experience debugging distributed systems and optimizing network performance
- Experience with accelerator orchestration (e.g., XManager)
Tasks
- Design and implement scalable infrastructure for deep learning models
- Optimize data loading and training speed for over 1000 GPU jobs
- Build data pipelines for distributed computing of robotics data
- Develop APIs and tools for integrating machine learning techniques
- Lead efforts to open source models and engage with the community
- Optimize compute resource allocation to reduce costs and latency
- Create orchestration workflows for running jobs on GKE
- Develop tools for model understanding and analysis
Work Experience
- 2 years
Education
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- C++
- Docker
- Kubernetes
- TensorFlow
- JAX
- PyTorch
- Google Cloud Platform
- Kubeflow
- CUDA
- XManager
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Intrinsic and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
About the Company
Intrinsic
Industry
Other
Description
The company aims to reimagine the potential of industrial robotics, making it intelligent and accessible for businesses and developers.
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