דרושים » אופטיקה » Physicist & Optical Scientist - Q

Define experimental and theoretical research strategies to characterize light-tissue interactions, optical penetration, and thermal/mechanical effects in biological media.
Design, build, and operate experimental lab setups to investigate photo-induced tissue damage (e.g., dermal and ocular tissue models), defining precise biological damage thresholds and exposure margins.
Serve as the team's foremost authority on laser safety, leading formal laser classification (e.g., IEC 60825, ANSI Z136, FDA CDRH) and product safety compliance.
Translate complex biological damage models and physical limits into actionable system-level specifications, hardware constraints, and operational safeguards.
Define and design system level solutions for safety compliance.
Develop and validate analytical and numerical simulation models (e.g., Monte Carlo light transport, thermal bio-heat equations) to predict tissue response and optical safety margins.
Analyze and report empirical findings clearly, enabling cross-functional engineering teams to make rigorous, risk-informed architecture decisions.
Partner with multidisciplinary teams across hardware, firmware, optics, and human factors to drive feasibility from concept to safe, viable product implementations.

Responsibilities
Lead the biological and physical risk assessment for novel optical concepts, pinpointing exposure limits, hazards, and mitigation pathways.
Conduct hands-on lab experiments combining lasers and biological tissue models to measure, observe, and quantify damage mechanisms under varied irradiation profiles.
Formulate and own the formal laser product classification strategy, defining engineering controls, interlocks, and compliance documentation.
Characterize system-level physical limits and optical delivery parameters (wavelength, pulse duration, fluence, beam profile) to maximize performance while strictly adhering to safety standards.
Author comprehensive safety reports, compliance dossiers, and technical whitepapers to support engineering choices and regulatory alignment.

We are looking for a Physics & Optical Simulation Software Engineer to build and scale our simulation platforms and data pipelines that power our physical modeling and sensor research. While the domain involves physics and optics, this is a software-first role: you will architect modular, high-throughput simulation systems that turn theoretical formulations into production-grade software tools. We value strong software engineering discipline, expertise in Python, and the intellectual agility to quickly master new scientific domains and innovate.

Responsibilities
Architect, build, and maintain robust, end-to-end simulation pipelines and scalable developer platforms.
Design modular, high-throughput simulation workflows and infrastructure in Python and modern scientific stacks.
Partner with physicists and optical engineers to translate physical models, wave-propagation algorithms, and numerical solvers into performant, clean software modules.
Optimize simulation frameworks for performance, scalability, data throughput, and maintainability.
Develop verification suites, benchmarking frameworks, and automated regression testing pipelines to validate simulation outputs against ground-truth and physical measurements.
Establish best practices for simulation software: modular packaging, version control, CI/CD, and technical documentation within a research-driven engineering environment.

We are seeking a creative and hands-on Deep Learning Research Engineer to drive model and architecture innovation across our sensing platforms. In this role, you will go beyond off-the-shelf models, exploring novel neural network designs, designing rigorous training and validation pipelines, and translating inventive concepts into clean, high-performance implementations. While our domain deals with physical data, this is an ML-first role: we are looking for a deep learning specialist with strong research intuition, expertise in PyTorch, and the drive to solve non-standard, real-world problems.

Responsibilities
Conceive, design, and implement novel neural network architectures tailored for complex physical sensing, reconstruction, and inverse problems.
Lead end-to-end hands-on research: data preparation, model formulation, training, hyperparameter exploration, and failure-mode analysis.
Build, train, and rigorously validate deep learning models using Python and PyTorch.
Formulate custom loss functions, regularization techniques, and inductive biases that capture domain-specific constraints.
Design comprehensive validation and benchmarking suites to stress-test model robustness, generalization, and scaling behavior.
Propose creative, non-conventional modeling ideas, stay abreast of state-of-the-art DL literature, and rapidly prototype proof-of-concepts.
Collaborate cross-functionally with physicists and optical software engineers to benchmark models against simulated and experimental lab datasets.

Every camera and sensing system we ship is used, every day, by hundreds of millions of people who simply expect it to work. Ensuring our systems are worthy of that expectation is this team's goal. We challenge products through the physical world to prove it, and to feed real-world evidence back into system design - measuring and analyzing the performance of multi-disciplinary optical sensing systems. This role is the analytical core of that work. You will define what we measure and why, mine large volumes of measurement data for the effect that actually matters, and take a question that has never been answered to a defensible answer. It is highly collaborative: you will work directly with module, optics, algorithm and system teams, and your conclusions will be the basis of their decisions. You will work alongside our lead system engineer as a peer, with problems allocated between you according to where the issue actually sits. We are looking for someone who genuinely enjoys digging into data until it gives up what it knows, and who wants to take that on with us.
Responsibilities
Own the characterization of system performance end to end - from risk and failure mode mapping through methodology definition, execution, analysis and reporting
Define metrics, testing & measurement methodology
Mine large data sets to separate isolate performance root causes
Correlate physical measurement to human perception - where the HW meets the user
Build the analysis capability the work requires - pipelines, tooling methods
Present and defend findings to module, optics, algorithm and system teams

We are seeking a Senior Systems Engineer for our System of Systems project, focused on developing innovative aerospace solutions. You will lead systems integration efforts and ensure seamless functionality across various systems.
Responsibilities:
Own the systems engineering of a complex System of Systems (SoS), maintaining the overall system-level view across multiple interconnected systems and subsystems.
Translate operational and customer needs into system architecture, clear and verifiable requirements, and system and interface specifications.
Define and manage interfaces and cross-system dependencies across radar, electro-optic, software and hardware teams.
Lead system integration, verification and validation activities, including system-level troubleshooting and resolution of technical issues, and participate in field integration and test campaigns (ground and live-fire test events).
Identify system risks, interface gaps and integration challenges early, and drive technical solutions with the relevant engineering teams.
Participate in and contribute to system design reviews (SRR, PDR, CDR) and technical trade-off studies from a System of Systems perspective.
Serve as the technical focal point for system-level and cross-system topics, working closely with multidisciplinary engineering teams and reporting key progress, risks and decisions to the Head of Systems Engineering.

BIRD Aerosystems is seeking a Senior Systems Engineer for our System of Systems project, focused on developing innovative aerospace solutions. You will lead systems integration efforts and ensure seamless functionality across various systems. Responsibilities:
* Own the systems engineering of a complex System of Systems (SoS), maintaining the overall system-level view across multiple interconnected systems and subsystems.
* Translate operational and customer needs into system architecture, clear and verifiable requirements, and system and interface specifications.
* Define and manage interfaces and cross-system dependencies across radar, electro-optic, software and hardware teams.
* Lead system integration, verification and validation activities, including system-level troubleshooting and resolution of technical issues, and participate in field integration and test campaigns (ground and live-fire test events).
* Identify system risks, interface gaps and integration challenges early, and drive technical solutions with the relevant engineering teams.
* Participate in and contribute to system design reviews (SRR, PDR, CDR) and technical trade-off studies from a System of Systems perspective.
* Serve as the technical focal point for system-level and cross-system topics, working closely with multidisciplinary engineering teams and reporting key progress, risks and decisions to the Head of Systems Engineering.

We are looking for a scientist who can turn experimental results into deep physical insight. Working closely with experimental teams gathering raw data, you will analyze multi-source datasets to uncover the connections between signals, derived features, and system-level performance metrics. You will combine data-driven modeling with first-principles physical intuition to explain performance dynamics and isolate the root causes driving them.

Responsibilities
Analyze large, multi-source datasets of signals and features. Build scalable, reproducible pipelines.
Find and quantify relationships between signal characteristics, extracted features, and performance outcomes.
Apply non-linear and data-driven methods to uncover effects that linear analysis misses.
Build and validate models that explain or predict performance from signal-level and feature-level inputs.
Assess data quality and measurement uncertainty. Recommend which measurements or experiments to run next.
Separate causal effects from correlations through ablations and structured analysis.
Present findings and recommendations clearly to physics, algorithm, and systems teams.
Flag inconsistencies in the physics or the data early.

We are seeking an experienced Signal/Image Processing and Machine Learning engineer to join our team. The role focuses on developing innovative, real-time deep learning and signal processing solutions for new sensing and imaging systems, including video restoration. You will be part of an interdisciplinary team developing technologies that will shape future Apple products.
Description
Algorithm and model development:
- Design, implement, and optimize signal processing and machine learning models for sensing and imaging tasks, including video restoration.
- Develop and validate models using real sensor data, simulated data, and public datasets.
- Prepare, filter, process, and analyze large-scale datasets to train and evaluate models.
- Analyze your algorithm's results and benchmark them against leading methods, and theoretical bounds.
- Optimize models for real-time performance, low latency, and resource efficiency on-device.

Sensing and research:
- Develop algorithms that combine classical signal processing with learned models to extract robust information from sensor data.
- Investigate efficient architecture patterns (lightweight backbones, attention mechanisms, recurrent designs) suitable for real-time, on-device inference.
- Explore how algorithmic approaches can mitigate artifacts and limitations of new sensing and imaging systems.
- Stay current with recent advances in signal processing, computer vision, and machine learning.

Collaboration and technical leadership:
- Work closely with ML researchers, SW, HW, sensor, and architecture engineers to translate research ideas into robust models and practical, shipping systems.
- Provide technical guidance and mentorship to junior engineers, and help shape the team's technical direction.

Responsibilities
- Own the end-to-end development of real-time algorithms, from concept and prototyping through validation and deployment.
- Define evaluation methodology, metrics, and failure analysis processes for sensing and imaging algorithms.
- Drive trade-off decisions among accuracy, latency, power, and memory across algorithm, HW, and system constraints.

We are seeking an experienced Signal/Image Processing and Machine Learning engineer to join our team. The role focuses on developing innovative, real-time deep learning and signal processing solutions for new sensing and imaging systems, including video restoration. You will be part of an interdisciplinary team developing technologies that will shape future Apple products.
Description
Algorithm and model development:
- Design, implement, and optimize signal processing and machine learning models for sensing and imaging tasks, including video restoration.
- Develop and validate models using real sensor data, simulated data, and public datasets.
- Prepare, filter, process, and analyze large-scale datasets to train and evaluate models.
- Analyze your algorithm's results and benchmark them against leading methods, and theoretical bounds.
- Optimize models for real-time performance, low latency, and resource efficiency on-device.

Sensing and research:
- Develop algorithms that combine classical signal processing with learned models to extract robust information from sensor data.
- Investigate efficient architecture patterns (lightweight backbones, attention mechanisms, recurrent designs) suitable for real-time, on-device inference.
- Explore how algorithmic approaches can mitigate artifacts and limitations of new sensing and imaging systems.
- Stay current with recent advances in signal processing, computer vision, and machine learning.

Collaboration and technical leadership:
- Work closely with ML researchers, SW, HW, sensor, and architecture engineers to translate research ideas into robust models and practical, shipping systems.
- Provide technical guidance and mentorship to junior engineers, and help shape the team's technical direction.

Responsibilities
- Own the end-to-end development of real-time algorithms, from concept and prototyping through validation and deployment.
- Define evaluation methodology, metrics, and failure analysis processes for sensing and imaging algorithms.
- Drive trade-off decisions among accuracy, latency, power, and memory across algorithm, HW, and system constraints.

Join the R&D team at BIRD Aerosystems as an EO System Engineer, where you will be responsible for developing and maintaining state-of-the-art electro-optical systems for aerospace applications.
Responsibilities: Design and develop electro-optical systems for aerospace applications. Collaborate with cross-functional teams to integrate systems into larger platforms. Conduct testing and validation of EO systems to ensure optimal performance. Troubleshoot and resolve technical issues related to EO systems. Document system specifications, designs, and testing procedures. Stay updated with the latest advancements in EO technology. Provide technical support and guidance to other team members.