דרושים » תוכנה » Machine Learning Data Scientist

We are looking for a talented and curious Audio Machine Learning Engineer to join our growing Machine Learning team in Herzliya. In this role, you will help create the full data lifecycle that underpins our models: from designing what data we collect, through curation and quality monitoring, to running rigorous experiments that drive model improvements. You will work closely with other ML and Data Engineering teams to ensure our models are trained on the best possible data, reaching the best accuracy, and that we deeply understand when and why they don't perform as expected.

Redefine the future of human-computer interaction and the way people communicate. Contribute to products that shape mobile computing and create breakthrough technologies in the audio domain.
In this role, you will push the boundaries of audio solutions across the full stack - from data pipelines and model training to optimization for our silicon. You'll collaborate with world-class researchers and engineers to ship technology that reaches hundreds of millions of users, while upholding our unwavering commitment to privacy.

Responsibilities
Work with unique, proprietary datasets - developing algorithms to process them and devising metrics to evaluate and improve quality.
Design and implement machine learning models focused on the audio domain, for low-latency feedback and high-fidelity streaming.
Drive data quality insights and influence the design of our end-to-end system.
Conduct both cutting-edge research and product-oriented development.
Collaborate closely with researchers, engineers, and product teams to bring new capabilities to life.

Play a part in shaping the future of human communication technology. Contribute to a unique multidisciplinary system that models and understands human interaction, redefining what's possible with computer vision, physics, and signal processing at the edge.

In this role, you'll be at the forefront of a one-of-a-kind technical challenge, developing and implementing novel methods for processing and enhancing a proprietary sensor that models human communication. You'll work hands-on with exclusive data: designing the algorithms that process it, and defining the metrics that evaluate and drive its continuous improvement.

Your insights will carry real weight, directly informing sensor decisions and shaping the architecture of the broader system. You'll lead multi-level research efforts to advance a truly unique sensor, drawing on a rich and diverse technical toolkit spanning signal processing, computer vision, physics, and state-of-the-art deep learning. You'll own proprietary data collections using high-end computer vision techniques, studying signals from their raw-level behavior all the way through to their top-level impact on product performance.

This is a role that lives at the intersection of deep research and real-world impact. You'll conduct cutting-edge investigations and translate your findings directly into product outcomes, influencing decisions across the full stack, from hardware choices and algorithmic pipelines to the features that reach the final product.

Responsibilities
Develop and implement novel algorithms for modeling and understanding human communication, combining 2D/3D computer vision, signal processing, and deep learning.
Work with unique proprietary datasets - design large-scale data processing pipelines, define quality metrics, and provide actionable feedback to improve data collection and labeling workflows.
Devise and implement rigorous evaluation frameworks to measure model and data quality, and drive continuous improvement across the system.
Design neural network architectures optimized for SOTA accuracy and computational efficiency.
Stay current with the latest research across computer vision, signal processing, and efficient ML; evaluate and integrate relevant advances into the team's work.
Contribute to internal tooling and best practices for reproducible, scalable ML research and deployment.

Play a part in shaping the future of human-computer interaction. Contribute to products that are redefining mobile computing and creating breakthrough technologies in conversational AI, speech recognition, and natural language understanding and generation.

We are seeking a passionate and experienced Machine Learning Engineer to join our team. In this role, you will push the boundaries of on-device speech recognition and NLP, working across the full stack - from data pipelines and model training to optimization for our silicon. You'll collaborate with world-class researchers and engineers to ship technology that reaches hundreds of millions of users, while upholding our unwavering commitment to privacy.

Responsibilities
Work with unique, proprietary datasets - developing algorithms to process them and devising metrics to evaluate and improve quality.
Design and implement machine learning models spanning speech recognition and NLP domains.
Drive data quality insights and influence the design of our end-to-end system.
Conduct both cutting-edge research and product-oriented development.
Collaborate closely with researchers, engineers, and product teams to bring new capabilities to life.

Join our team as a Machine Learning Engineer and help shape the future of on-device AI. You'll research, design, and deploy cutting-edge deep learning models optimized for our silicon edge devices, working across the full ML lifecycle alongside hardware, software, and product teams.

We are looking for a talented and motivated Machine Learning Engineer to join our team. You will work within a collaborative, research-driven engineering culture that values innovation and rigor, with the opportunity to build impactful AI products deployed at scale on real devices. We offer competitive compensation, benefits, and opportunities for professional growth.

Responsibilities
Research and design state-of-the-art deep learning models optimized for resource-constrained our silicon edge devices.
Drive projects across the full ML lifecycle, from ideation and experimentation to production deployment.
Collaborate closely with cross-functional teams including hardware, software, and product.
Continuously evaluate and adopt new techniques to improve model performance and efficiency on-device.

We are seeking a Machine Learning expert to join our wireless FW software group and explore innovative applications of ML algorithms in embedded wireless systems and protocols. This role focuses on cutting-edge research to integrate machine learning into wireless firmware architectures as well as protocol design, validation, and performance optimization. The position emphasizes experimental research, algorithm development, and proof-of-concept implementations in the wireless FW domain. This role offers the opportunity to develop novel solutions that bridge the gap between theoretical machine learning capabilities and practical wireless system implementations.
Responsibilities:
Research & Development: Design, develop, and evaluate machine learning algorithms optimized for real-time wireless communication systems operating under strict real-time, power and computational constraints.
Protocol Innovation: Create novel wireless protocols that intelligently incorporate ML techniques for enhanced performance, efficiency, and adaptability.
Algorithm Optimization: Adapt existing wireless algorithms (e.g. Link Quality, QoS, Scheduling etc) by integrating ML approaches while maintaining real-time performance requirements.
Data Collection & Analysis: Gather, analyze, and curate statistics from existing wireless devices and networks to enable ML model training and validation. Design and implement data collection frameworks for various wireless environments and use cases.
System Integration: Develop lightweight ML models suitable for deployment on resource-constrained wireless devices, including edge computing scenarios.
Performance Analysis: Conduct comprehensive performance evaluations through simulation, theoretical analysis, and experimental validation.
Research Publication: Publish findings in top-tier conferences and journals, and present research at industry conferences.
Collaboration: Work closely with hardware engineers, system architects, and product teams to ensure research translates into practical solutions.
Technology Scouting: Stay current with emerging trends in ML, wireless communications, and edge computing technologies.

Were looking for a data scientist to join our team and further improve fraud detection capabilities.
This position involves analyzing evolving fraud patterns, developing and deploying advanced prevention components, and leveraging domain expertise, data tools, and modern AI methods to stay ahead of emerging threats.
A partial list of our tech stack: Python (and its related scientific stack), Spark, TensorFlow/PyTorch, Kafka, AWS services, Airflow, Scala, BigQuery, and more
What you'll do:
Mine massive data sets to uncover fraud patterns and prevention opportunities.
Develop and productionize large-scale ML, DL, and AI models to detect and prevent fraudulent activity.
Translate business and product needs into scientific, explainable, and high-impact solutions.
Design and maintain end-to-end ML pipelines - from data ingestion and feature engineering to serving and monitoring in production.
Collaborate closely with product, engineering, and business teams to drive data-driven decisions.
Stay current with AI and ML research and evaluate new technologies to strengthen Protect360s algorithms.

At our company, we see the world of cybersecurity differently. Instead of chasing threats, we believe the most practical approach is protecting data from the inside out. Weve built the industrys first fully autonomous Data Security Platform to help our customers dramatically reduce risk with minimal human effort.
At our company, we move fast. Were an ultra-collaborative company with brilliant people who care deeply about the details. Together, were solving interesting and complex puzzles to keep the worlds data safe.
We work in a flexible, hybrid model, so you can choose the home-office balance that works best for you.
Responsibilities
Design and Build ML Infrastructure: Develop and maintain scalable, production-ready infrastructure for both traditional ML (anomaly detection, user behavior analytics) and LLMs across enterprise environments.
Optimize Model Performance: Analyze and optimize LLM and ML performance using techniques like knowledge distillation, quantization, and efficient data structures to boost efficiency and lower resource costs.
Deploy and Integrate: Collaborate heavily with software and data engineers to integrate models into production pipelines, cloud-native environments, and on-premises workflows.
Drive MLOps & Tooling: Manage the complete model lifecycle (monitoring, retraining, deployment) and actively build custom tools from scratch to improve the team's ML workflows.
Elevate Engineering Standards: Perform rigorous code reviews, ensure robust Python production standards, and provide technical guidance to data scientists and junior engineers.
Cross-Functional Partnership: Partner with cybersecurity researchers and product teams to translate research insights and threat analysis features into highly performant production code.
Open-Source Engagement: Actively engage with the open-source community by contributing code and expertise to relevant ML/LLM projects.

In your role as a WiFi ML Link Adaptation Algorithm Engineer, as part of our Connectivity Group, you will be part of a world-class group that pioneers design and development of MAC Layer algorithms for wireless communication systems on our products.

We are looking for ML algorithm engineer to innovate and develop advanced rate adaptation and link optimization algorithms for WiFi systems. You will design and develop intelligent algorithms that dynamically optimize wireless transmission parameters to maximize throughput and reliability across diverse channel conditions.

Responsibilities:
Develop algorithms and models to improve rate selection, frame sizes, antenna selection and more with using ML.
Build/Configure simulation environments modeling fading channels, interference, and multi-user scenarios.
Validate performance of algorithms and models in network and HW simulations across diverse channel conditions.
Convert models and algorithms to embedded firmware implementation.
Create comprehensive test frameworks to evaluate real-world performance.
Conduct over-the-air testing and performance characterization on devices.
Analyze field telemetry data to identify optimization opportunities.
Debug and resolve algorithm-related issues in real-world deployment scenarios.

We are looking for a Senior Product Manager to join our Analytics team within the Measurement group. In this role, you will own a core analytics domain used daily by global advertisers to measure, interpret, and optimize their marketing performance. You will work at the intersection of large-scale data, attribution complexity, privacy-driven measurement changes, and AI-powered capabilities - shaping how customers understand and rely on their marketing data.
This is a high-impact, highly visible role with direct influence on customer trust, product strategy, and the evolution of AppsFlyers analytics platform.
What youll do:
Own a core Analytics domain end-to-end - from discovery and strategy definition to execution, launch, and continuous improvement.
Define and drive the roadmap for dashboards, metrics, and data experiences serving enterprise marketing teams.
Balance stability, scalability, and innovation in a complex, data-intensive environment.
Lead initiatives that modernize and consolidate analytics capabilities across multiple attribution sources.
Shape the integration of AI-powered capabilities into analytics experiences, enhancing how customers interpret performance data.
Work closely with R&D (Backend, Frontend, Data Engineering) to build scalable, high-performance analytics products.
Partner with Sales and Customer Success to understand customer needs, support adoption, and strengthen trust in our measurement solutions.
Engage directly with customers to validate product direction and identify opportunities to improve clarity, usability, and performance insights.
Drive prioritization in a high-demand environment with multiple stakeholders and competing initiatives.

We are looking for a Software Engineer specializing in ML systems Engineering to join Development department.
While vision and language models have become increasingly commoditized, proprietary deep learning models are unique, fast-evolving, and deployed in live trading across the worlds most efficient and sophisticated financial markets. Operating in this environment presents distinct scaling challenges and continuous opportunities for optimization. Success in this role requires first-principles thinking and a deep understanding of the engineering trade-offs behind high-performance DL systems.
This is a pivotal role within engineering organization. You will work closely with researchers and engineers across the company, running deep learning models on massive compute clusters and adapting them for production serving under strict and non-trivial constraints.