Position Description: ML/DL and Algorithm Engineer

Company: Sensorz

Location: Kfar Saba

About Us: Sensorz  introduces a revolutionary Wireless Observability Platform (WOP) that autonomously detects anomalies and utilizes AI for threat detection and classification across all wireless transmissions. Our intuitive dashboard seamlessly integrates with both NOC and SOC, showcasing Sensorz's unique capabilities:

• Decoding wireless protocols in conjunction with RF signals for complete observability

• Working in very “noisy” environments and “understanding” the wireless spectrum

• Running proprietary patent-pending algorithm to classify wireless threats and events helping our customers to better utilize their spectrum and their wireless systems.

Position Overview: We are seeking an experienced ML/DL Algorithmic Engineer with a robust knowledge of machine learning and deep learning algorithms, especially in anomaly detection, ML/DL algorithms on graphs (e.g., GNNs), classification, and segmentation focusing on the few-shot regime, advances data generation and augmentation methods.

A thorough knowledge of localization algorithms both in theory and practice and for both outdoor and indoor solutions is a major advantage.

The ideal candidate will have an in-depth understanding of the entire ML lifecycle, from research and development to deployment, and will be instrumental in driving our projects forward, pushing the boundaries of what's possible.

Key Responsibilities:

·        Algorithm Development and Implementation:

o  Design, implement, and optimize ML/DL algorithms.

o  Specialize in areas like anomaly detection, GNNs, segmentation, and classification, object detection, etc.

o  Convert innovative ideas from literature into practical algorithms and design custom solutions from scratch if needed.

·        Coding & Frameworks:

o  Deliver high-quality code and collaborate on code reviews.

o  Handle various ML/DL frameworks to enhance our capabilities.

·        Model Testing & Performance Assessment:

o  Design robust testing methodologies for ML models to ensure reliability and robustness.

o  Evaluate model performance using appropriate metrics and tools, ensuring the highest level of accuracy and efficiency.

·        ML Lifecycle & Deployment:

o  Understand and manage the complete lifecycle of ML models from data collection and preprocessing to training, evaluation, and deployment.

o  Collaborate with the infra / cloud team to integrate and deploy models into production systems.

·        Research & Development:

o  Stay updated with the latest advancements in ML/DL and related fields.

o  Engage in continuous learning, reading research papers, and implementing new methodologies.

o  Ability to establish a solution from scratch inunder uncertainty conditions.

o  Prioratizing well between tasks/ subtasks and meeting timelines

·        Team Collaboration & Leadership:

o  Work effectively with cross-functional teams and communicate complex ideas clearly.

o  Lead by example, mentor junior team members, and drive collaborative projects to success.

Qualifications:

·        At least 5 years of experience in industry

·        Bachelor's in computer science, Electrical Engineering, or a related field.

·        Master’s/ PhD – is an advantage

·        Deep knowledge of Python and proficiency in various ML/DL frameworks like Pandas /Sklearn / Pytorch / Tensorflow etc,...

·        Demonstrable experience in localization techniques, with an emphasis on both indoor and outdoor environments – is a significant advantage.

·        Familiarity with communication/RF technologies is an advantage.

·        Knowledge in MLOPs and ML lifecycle – is a significant advantage.

·        Experience with training and serving ML models at scale – an advantage.

·        Familiarity with Git and version control tools – an advantage.

·        Strong problem-solving skills, a "can-do" approach, out of the box thinking, and a passion for continuous learning.

·        Excellent team collaboration skills.

·        Ability to handle projects end-to-end, from ideation and research to deployment.