Machine Learning Engineer
Role details
Job location
Tech stack
Job description
We are looking for a Machine Learning Engineer to support the ML team in bringing state-of-the-art machine learning models to production.
We work on object detection, scene understanding, self-supervised learning, temporal models, representation learning, model compression, efficient architectures and many other exciting topics.
You will contribute to a broad spectrum of topics ranging from implementing research ideas over improving our training and deployment pipelines to enhancing our data quality and efficiency.
You will cover all phases of the ML life cycle and production-grade development
- Assess and solve new ML use cases
- Go from scoping & design to production
- Build and improve our internal ML framework
- Automate and stabilize our training, evaluation and deployment pipelines
- Build new ML models and efficient architectures
- Build tools and use the tools you build
- Exploratory data analysis, auditing, build tools for auto-labeling
- Maintain and extend our ML data pipelines
- Manage our on-premise and cloud storage and compute resources (GCP)
We value intelligence, curiosity and a solution-driven mindset higher than existing skills.
Requirements
Still, for this role we expect you to have experience in:
- Software engineering (Python)
- Deep learning for Computer Vision
- PyTorch
- Model deployment and optimization with ONNX Runtime and TensorRT
- Parameter / model studies, managing experiments
- On the side: SQL, Git, Linux
Additionally, it would be great if you already have experience in any of the following:
- Computer Vision
- Understanding and implementing research papers
- Efficient Deep Learning / Model Compression / Knowledge Distillation
- Data & annotation management
- Model performance optimization for NVIDIA hardware platforms
- Generative Models for text, image and video
- TensorRT / ONNX & ONNX Runtime
- CI / CD
- Docker / Containers
- Cloud storage and computer
