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We are seeking a driven Deep Learning Engineer who thrives working in a fast-paced development environment. In this role, you will be part of a team that creates deep learning processes from the concept stage through R&D and to productization. The ideal candidate will be up-to-date on the latest developments in AI/ML technology and have strong analytical and problem-solving skills and a sharp eye for detail.
Typical Duties and Responsibilities
- Create frameworks that are extremely scalable and effective for the various stages of the DL model life cycle
- Design and deploy large-scale systems for supervised and unsupervised model training paradigms
- Evaluate the current MLOPs procedures to identify shortcomings and take steps to address them
- Collaborate with infrastructure teams to develop tools and interfaces for quick benchmarking, model analysis and optimization, hyper-parameter tuning, and data visualization
Education
- Bachelor’s degree in computer science, statistics, mathematics or a related field
Required Skills and Experience
- 3+ years of experience developing and deploying deep learning models for computer vision issues including object recognition and segmentation
- Strong Python programming background
- Practical experience with at least one of the following: PyTorch, Tensorflow, Caffe2, or MXNet
- Knowledge of Kubernetes task management and containerization
- Expertise conducting research and development in pipelines for distributed large-scale training or automating intricate MLOps pipelines at consistent release intervals
- Knowledge of building and deploying machine learning pipelines using GCP, Azure ML, or AWS SageMaker
- Experience applying MLOps and DevOps best practices in an automotive/ADAS environment
- Experience with DL frameworks for convenience, such as PyTorch Lightning or DL-Catalyst
- Knowledge of alternative training time model compression techniques as well as large scale AutoML pipelines for Neural Network Architectural Search
- Experience working in a hurried development environment
- Excellent teamwork and communication abilities
Preferred Qualifications
- Master’s degree or higher in computer science, statistics, mathematics or a related field