Deep Learning Engineer

Deep Learning Algorithms Engineer

About CorrActions 

CorrActions is at the forefront of enhancing road safety by equipping vehicles and fleets with the capability to detect and respond to drivers' cognitive impairments due to factors such as alcohol, fatigue, drugs, and stress. Our groundbreaking work is supported by industry giants like Volvo Cars, Goodyear, and Blackberry, highlighting our commitment to innovation and safety in the automotive sector. 

Job Description

As a Deep Learning Engineer at CorrActions, you will be at the forefront of cognitive state research, developing cutting-edge deep learning algorithms and models for cognitive states detection with real-world impact on road safety. You will collaborate with a team of experts in a dynamic and fast-paced environment to push the boundaries of what is possible in the realm of cognitive state monitoring.

Key Responsibilities:

• Design and develop advanced deep learning algorithms and models.
• Optimize models for maximum performance, scalability, and efficiency.
• Collaborate with cross-functional teams to integrate AI solutions.

Qualifications:

• Masters or PhD in Biomedical Engineering, Computer Science, Electrical Engineering, or a related field.
• Background in Deep Learning theory, and track record of publications at conferences such as CVPR, ICML, NIPS, ICCV, ECCV, and others
• Proven track record of developing and successfully implementing deep learning models for business applications.
• Proven Statistical Analysis, Mathematical, and Problem-Solving skills.
• Ability to thrive in a fast-paced, innovative environment.
• Excellent communication skills with the ability to work collaboratively in a team.

Required Skills:

• Deep Learning: Expertise in neural network architectures and training techniques.
• Multivariate Time Series Analysis: Ability to handle and interpret complex temporal data.
• Data Analysis: Proficient in extracting insights from large datasets.
• Computational Neuroscience: Understanding of brain-inspired algorithms and models.
• Programming: Proficiency in Python and experience with Pytorch.
• MLOps: Familiarity with MLOps practices and tools like ClearML.
• Cloud Computing: Experience with AWS for deploying scalable machine learning solutions.
• Version Control: Proficiency in Git for source code management.