Data Scientist

About the job

About Us

Ejrider is a commercialization catalyst for breakthrough inventions that create transformational industry impact and improve lives on a global scale. Our scientists and business leaders bring our signature methodology and perspective to identifying, acquiring and commercializing promising intellectual property in industries that have gone without meaningful innovation in decades.

ejrider Israel serves as the research and development arm of ejrider Global,

we specializes in the research, development, and manufacturing of cutting-edge technologies based on terahertz waves, offering groundbreaking solutions across various industries.

What does ejrider Israel do?

ejrider Israel acts as the innovation and technology hub of the group, focusing primarily on the development of terahertz wave-based technologies.

What are terahertz waves?

Terahertz waves occupy a less-explored region of the electromagnetic spectrum. ejider Israel leverages their unique properties to develop innovative and transformative solutions.

Unlike conventional technologies such as X-rays, infrared (IR), or MRI, terahertz waves offer high precision, rapid scanning capabilities, and non-invasive detection, requiring minimal material to identify and sense targets.

We are seeking a highly skilled Machine Learning Engineer/Scientist to join our R&D team. The ideal candidate will have hands-on experience with large-scale data infrastructure, a strong background in deep learning (particularly with PyTorch), and the ability to design and run complex experiments in a systematic and reproducible way.

Key Responsibilities

Data Infrastructure & Accessibility

• Work with MongoDB, AWS, and other big data technologies to store, manage, and retrieve large-scale datasets efficiently.
• Define computational requirements (CPU, GPU, memory, etc.) and select optimal machines/environments (on-premise or cloud) for experiments.

Deep Learning & Experimentation

• Develop, train, and fine-tune state-of-the-art deep learning models using PyTorch.
• Arrange data into accessible pipelines for rapid experimentation.
• Conduct multiple ML experiments in parallel, adjusting hyperparameters and architectures to achieve target metrics.

Automation & Workflow

• Create robust automation scripts for experiment orchestration (e.g., job scheduling, environment setup).
• Implement pipelines to automatically track, log, and archive experiments for reproducibility.

Performance Analysis & Reporting

• Use tools such as Weights & Biases (wandb) or CometML to log metrics and compare results across experiments.
• Generate clear, data-driven reports and visualizations to communicate findings and progress to technical and non-technical stakeholders.

Research & Development

• Leverage strong foundations in math, physics, and deep learning theory to explore innovative model architectures or approaches.
• Stay current with emerging techniques, libraries, and best practices in the ML community.

Required Qualifications

●     Education: Bachelor’s or Master’s in Engineering, Computer Science, Physics, Mathematics, or a related field.

●     Mathematics & Physics: Strong theoretical background—must understand the foundations behind deep learning (linear algebra, calculus, probability, etc.).

●     Deep Learning Expertise: Hands-on experience with PyTorch for model development and training.

●     Big Data Experience: Familiarity with managing large datasets in MongoDB, AWS, or similar cloud infrastructure.

●     Data Engineering: Competence in organizing and preprocessing large datasets for efficient training and inference.

●     Experiment Tracking: Familiarity with wandb, CometML, or other experiment tracking/reporting tools.

Preferred Qualifications

●     Cloud Experience: Hands-on with AWS services (EC2, S3, ECS/EKS, Lambda) or similar cloud platforms.

●     Automation & Tooling: Experience with CI/CD, workflow orchestration (Airflow, Luigi, etc.), or containerization (Docker).

●     Experimentation & Parallelization: Demonstrated ability to run multiple experiments simultaneously (e.g., cluster computing, Docker/Kubernetes, job schedulers).

Soft Skills

●     Communication: Ability to clearly communicate complex technical findings to diverse stakeholders.

●     Collaboration: Comfortable working with cross-functional teams (data scientists, physicists, domain experts).

●     Ownership & Initiative: Self-driven, takes responsibility for end-to-end project execution.

●     Curiosity & Innovation: Keen interest in experimenting with new techniques and pushing state-of-the-art solutions.