דרושים » תוכנה » Interdisciplinary Researcher

We are a growing physics team working at the intersection of cutting-edge research and real-world product impact. We are looking for an exceptional Physicist or Electrical Engineer to join us in assessing the feasibility of a groundbreaking, first-of-its-kind technology, one with the potential to reshape the industry.

Define research topics and experimental strategies aligned with system and product requirements.
Design, build, and operate experimental and test setups to measure and characterize system performance.
Collect, analyze, and interpret experimental data, translating insights into technology capabilities and product specifications.
Investigate new methods, theories, and physical phenomena; apply existing knowledge to novel challenges and generate new understanding through rigorous experimentation.
Characterize material and system-level physical limits to inform design feasibility and define system requirements.
Develop and validate simulation models to support experimental and systems work.
Analyze and report results clearly to enable cross-functional teams to make informed design decisions.
Partner with multidisciplinary teams across hardware, software, and systems engineering to drive research from concept to product.

We are growing our physics team, working at the intersection of research and real-world product impact. We are looking for a talented experimental physicist who is passionate about translating fundamental science into transformative technology.

Responsibilities
Define research topics and experimental strategies aligned with system and product requirements.
Design, build, and operate experimental and test setups to measure and characterize system performance.
Collect, analyze, and interpret experimental data, translating insights into technology capabilities and product specifications.
Investigate new methods, theories, and physical phenomena; apply existing knowledge to novel challenges and generate new understanding through rigorous experimentation.
Characterize material and system-level physical limits to inform design feasibility and define system requirements.
Develop and validate simulation models to support experimental and systems work.
Analyze and report results clearly to enable cross-functional teams to make informed design decisions.
Partner with multidisciplinary teams across hardware, software, and systems engineering to drive research from concept to product.

We are looking for an ML Engineer to join a research-oriented group developing physics-informed neural networks for advanced sensing and optical measurement systems. The role focuses on training, validating, and improving neural network models that combine data-driven learning with physical modeling, simulation, and experimental measurements.

Responsibilities
Develop, train, and evaluate neural network models for physics-based sensing and reconstruction tasks.
Incorporate physical knowledge into ML models through data generation, model architecture, loss functions, constraints, or regularization.
Work with simulated and experimental datasets, including data. preprocessing, augmentation, validation, and error analysis.
Build robust training pipelines using Python and modern ML frameworks such as PyTorch.
Compare model predictions against ground-truth data from simulations, optical experiments, or measurement systems.
Collaborate with physicists and optical engineers to understand underlying physical models and translate them into ML workflows.
Analyze model performance, identify failure modes, and improve generalization from simulation to real-world data.
Design experiments to evaluate model robustness, sensitivity, uncertainty, and accuracy.

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.

The system engineering team is responsible for developing, integrating, and building advanced sensing systems, combining cutting-edge modules with advanced algorithms across multi-disciplinary domains: electro-optical components, unique physical models, bio-mechanical considerations, mechanical and electrical trade-offs, dedicated SW & FW, large-scale data analysis, and classic and ML-based algorithms.

Responsibilities
Own the complete system life-cycle - from Proof-Of-Concept through preliminary design, detailed architecture, module builds, testability, and delivery to mass-production.
Risk mapping and mitigation activities.
Define data collection requirements.
Signal analysis and trade-off mapping.
System optimization.
Collaborate with multiple HW and SW teams to analyze and optimize overall system performance.
Define and track system specifications.
Lead multi-disciplinary teams to deliver reliable, high-performance system features.

As a Wireless Firmware Architect you will focus on defining technical direction, conducting research and analysis, and providing architectural guidance for our Bluetooth embedded solutions.

The position emphasizes wireless protocol design and simulations, software architecture definition, technical planning, proof-of-concept development and performance optimizations.

Responsibilities:
Define comprehensive software architecture for our Bluetooth embedded solutions.
Establish firmware architectural guidelines and principles.
Support development teams with architectural guidance and consultation.
Integrate performance and power analysis requirements into architectural frameworks.
Create technical demonstrations to support architectural decision-making.
Monitor and analyze hardware implementations to ensure alignment with software architectural requirements.
Evaluate hardware design decisions and their impact on firmware architecture.
Collaborate with hardware teams to provide architectural input and recommendations.
Design and develop proof of concept (PoC) projects to validate architectural approaches.
Research and prototype emerging wireless technologies and methodologies.
Evaluate feasibility of new technologies and integration approaches.
Design and develop wireless protocol simulation frameworks, develop intelligent simulation environments for protocol validation and optimization.
Analyze new standards and specifications for architectural implications.
Track industry trends and emerging wireless technologies.

We are looking for a highly skilled Senior Sensor & Embedded System Testing Engineer to join our 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.

The selected candidate will assume responsibility for the configuration of advanced camera and laser sensor systems, and for the rigorous validation of their functionality at the embedded level. This role encompasses interface verification, timing analysis, performance characterization, firmware integration support, and direct collaboration with research and physics evaluation programs.

Responsibilities
Lead the configuration and bring-up of camera and laser sensor subsystems within embedded platforms.
Define, develop, and execute comprehensive system-level validation and definition testing strategies.
Characterize sensor performance across optical, electrical, and timing domains.
Validate sensor interfaces (MIPI CSI-2, SPI, I²C, LVDS) and ensure compliance with system specifications.
Support firmware integration activities, ensuring robust sensor operation within the full system stack.
Design and maintain automated test infrastructure for sensor validation and regression testing.
Collaborate closely with physics, optics, and research teams to support experimental evaluation campaigns.
Perform root-cause analysis of issues spanning hardware, firmware, optics, and sensor subsystems.

We are looking for a highly skilled Senior Environmental System Test Engineer - Camera & Laser to join our team in Herzliya.

Responsibilities
Lead end-to-end environmental testing and characterization of camera and laser sensor systems, encompassing optical performance, mechanical alignment, and environmental robustness.
Define and execute environmental test protocols addressing real-world conditions, including: Solar/sun influence - direct and indirect illumination interference on sensor performance; Rain and moisture - water ingress, droplet effects on optical paths, condensation; Motion and walking scenarios - dynamic movement, vibration, and stabilization assessment.
Perform system-level validation ensuring sensor subsystems meet performance specifications across all environmental boundaries.
Design and build repeatable, controlled test environments and fixtures for rigorous physics-based evaluation.
Collaborate with cross-functional teams (optics, firmware, mechanical, and research) to translate environmental requirements into actionable test plans.
Analyze and report test data, providing clear recommendations for design improvements.

A hands-on engineering role focused on building production-grade AI/ML systems and the automation infrastructure that supports them - driving AI adoption into developer workflows, internal tooling, and domain-specific applications across the organization.

As a member of the AI Infrastructure & Applications team, you will lead the design, development, and production deployment of AI/ML-powered systems alongside the automation infrastructure and developer platforms that support them.
You will architect intelligent, scalable solutions used across the organization - driving AI adoption into developer and automation workflows, internal tooling, and domain-specific applications - while also building and maintaining the automation frameworks and infrastructure those systems depend on.
The systems you build are expected to be production-grade, reliable, observable, and continuously improving.

Responsibilities
Architect and ship end-to-end AI-powered applications and pipelines, from prototype to production.
Build agentic systems, RAG pipelines, and tool-use patterns that integrate LLMs into real workflows.
Define and own AI quality metrics (accuracy, groundedness, hallucination rate, task completion) and integrate them into CI/CD release gates.
Design evaluation frameworks for non-deterministic systems: offline evals, human-in-the-loop review, and automated regression suites.
Harness AI/LLMs to extend and enhance existing automation infrastructure, improving system performance and operational efficiency.
Build scalable automation frameworks, APIs, and tooling used across the organization.
Collaborate with engineering, CI, and domain teams to address automation needs across hardware, software, and cloud.
Distill requirements from a large, diverse user base into generic, reusable, maintainable solutions.
Implement monitoring, drift detection, and structured feedback pipelines for continuous improvement.
Apply rigorous engineering discipline - test design, release criteria, rollback strategies - to AI-native deployments.
Partner with product, design, and domain experts to define use cases, acceptance criteria, and rollout plans.

Play a part in shaping the future of human-computer interaction. As an MLOps Engineer, you will be the backbone of the machine learning infrastructure that powers our speech, audio, and conversational AI teams - ensuring their models are trained on the best possible data.
You will bridge the gap between research, data science, and engineering, owning the full ML lifecycle from large-scale data pipelines and distributed GPU training through to low-latency, high-fidelity inference and optimization. You'll partner closely with Audio ML Engineers, Speech ML Engineers, and ML Data Scientists to remove friction across their workflows and accelerate the path from research to product.

The MLOps Engineer will drive end-to-end quality and operational excellence across data ingestion, model training, deployment pipelines, and MLOps tooling for our speech and audio ML platforms. This hire will build, deploy, and optimize production-grade systems with a strong emphasis on scalable, GPU-accelerated infrastructure. You will own the training infrastructure that powers distributed and self-supervised model training on HPC and Slurm-managed clusters, as well as the inference pipelines that bring low-latency, high-fidelity audio and speech models to production. You will establish standard methodologies for model integration, deployment, monitoring, and reproducibility using CI/CD principles.

Responsibilities
Design, build, and operate large-scale data pipelines for proprietary audio and speech datasets - supporting curation, quality monitoring, and validation at scale alongside our ML Data Science team.
Partner closely with Audio ML Engineers, Speech ML Engineers, ML Data Scientists, and product teams to define metrics, gather requirements, and bring new capabilities to life.
Build and operate distributed GPU training workflows, including job scheduling and resource management on Slurm-managed HPC clusters, for both supervised and self-supervised methods.
Optimize model inference for low latency and high-fidelity streaming across serving environments, including optimization for Apple silicon.
Design and maintain automated pipelines for model training, evaluation, versioning, and deployment, with special attention to speech, audio, and signal-processing workflows.
Identify and resolve bottlenecks in ML and data workflows, improving system reliability, latency, and throughput at scale.