Deep Learning Engineer

A startup company in the Brain-Machine Interface field, integrating neuroscience with analytical solutions, primarily for the automotive industry. The company has developed a system for monitoring the driver's brain activity using sensors already present in the vehicle, such as steering wheel and seat sensors. These sensors monitor tiny involuntary movements of body muscles, influenced by brain activity and reflecting the driver's functional status.

The company is based in Tel Aviv, near a train line, with 25 employees, offering two days of remote work per week. They are funded and stable moving forward.

The role involves working as part of a team, primarily focusing on neural network architecture and DL/AI work, including model training, working with diverse data types including temporal, time series, neuroscience, and images, conducting research, reading papers, developing models, and also handling deployment to production in an AWS environment, large scale, MLOps, and Git. Coding is done in Python with PyTorch.

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:

● Master 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