ML Deployment Engineer, Edge AI - Q

לפני 30 דקות

ML Deployment Engineer, Edge AI - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

We are seeking a highly skilled and motivated ML Deployment Engineer to join our on-device AI team. In this role, you will take state-of-the-art machine learning models and make them run efficiently on Apple silicon - compressing, compiling, and optimizing them to deliver intelligent experiences that run entirely on-device, with the performance, power, and privacy our users expect.

Responsibilities
Own the model deployment pipeline from trained checkpoint to on-device execution, ensuring models meet product-quality bars for accuracy, latency, memory, and power.
Apply model compression techniques - quantization (post-training and calibration-based), palettization, pruning, and mixed-precision - and validate the accuracy/size/latency trade-offs of each.
Convert and compile models to the Core ML stack and target the Apple Neural Engine, GPU, and CPU.
Analyze and maximize accelerator (ANE) residency; diagnose why operations are rejected or dispatched to fallback compute units, and work with the graph to resolve them.
Profile end-to-end inference on real hardware, identify bottlenecks, and optimize compute-unit placement, memory footprint, and energy consumption.
Collaborate closely with ML research and applied ML to co-design models that are efficient to deploy and to feed hardware constraints back into model architecture decisions.
Build and maintain tooling and automation for reproducible compression, compilation, benchmarking, and numerical validation.
Stay current with the latest advances in efficient ML inference, model compression, and accelerator architectures.

Requirements:
Minimum Qualifications
Bachelors or Masters degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent practical experience.
5+ years of professional experience deploying machine learning models to production, with a significant focus on on-device, edge, or otherwise resource-constrained environments.
Strong programming skills in Python and C/C++.
Solid understanding of deep learning fundamentals and hands-on experience with a modern ML framework (e.g., PyTorch).
Practical experience with model compression and optimization - quantization, pruning, palettization, or mixed-precision - and a working understanding of floating-point and fixed-point/integer numerics and the quality trade-offs involved.
Experience taking models through a conversion and compilation toolchain to a hardware target (e.g., Core ML, TensorRT, TFLite, ONNX Runtime, or similar).
Demonstrated ability to profile and optimize code and models against strict latency, memory, and power constraints.
Strong debugging skills and comfort working at the boundary between ML models, compilers, and hardware.

Preferred Qualifications
Hands-on experience deploying to hardware accelerators / NPUs, and familiarity with the Apple Neural Engine (ANE), GPU, and CPU compute paths.
Experience with the Core ML toolchain and with inspecting or transforming model IRs (i.e MLIR) and writing custom graph passes.
Familiarity with reading and reasoning about compiler analytics - diagnosing operator fallback, layout, and residency issues on an accelerator.
Experience with calibration or data-based compression methods (e.g., GPTQ, activation quantization, sparsity) and layer-wise sensitivity analysis.
Background in on-device LLMs or generative models, including tokenization, KV-cache, and quantized inference.
Experience building benchmarking, numerical-validation, and CI tooling for model deployment.
Familiarity with low-level compute kernels, SoC memory hierarchies, and hardware-software co-design.
Excellent problem-solving skills, strong communication and teamwork, and a passion for building innovative products.

This position is open to all candidates.

Hide

עדכון קורות החיים לפני שליחה

8892475

שירות זה פתוח ללקוחות VIP בלבד

משרות דומות שיכולות לעניין אותך

דיווח על תוכן לא הולם או מפלה

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

29/10/2026

Senior Signal/Image Processing & Machine Learning Engineer - Sensing Technologies & Real-Time Models

חברה חסויה

Location: Herzliya and Haifa

Job Type: Full Time

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.

Requirements:
Minimum Qualifications
Strong foundation in signal processing, image processing, computer vision, machine learning, and deep learning.
Hands-on experience with sensing technologies (e.g., camera/image sensors, depth, radar, LiDAR, IMU, or other sensor modalities), including an understanding of sensor noise models and characteristics.
Demonstrated experience designing and deploying real-time deep learning and signal processing solutions using large-scale datasets.
Proficiency in Python and deep learning frameworks (e.g., PyTorch); experience with C++ or other performance-oriented languages is a plus.
Experience with model evaluation, debugging, experimental analysis, and failure analysis.
PhD or Master's degree in Electrical Engineering, Computer Science, or a related research field.
Proficient in written and verbal communication.
Ability to work both autonomously and collaboratively.
8+ years of relevant experience.

Preferred Qualifications
Experience deploying models on GPU, dedicated HW, or neural engines, including quantization, pruning, and other model compression techniques.
Experience with multi-sensor fusion, sensor calibration, or temporal/streaming signal processing.
Experience with generative priors (diffusion, flow matching) is an advantage.
Background in signal processing, physics, optics, lasers is a plus.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8877020

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

29/10/2026

Senior Signal/Image Processing & Machine Learning Engineer - Sensing Technologies & Real-Time Models

חברה חסויה

Location: Herzliya and Haifa

Job Type: Full Time

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8877032

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

לפני 47 דקות

Deep Learning Research Engineer - ML & Physics - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

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.

Requirements:
Minimum Qualifications
M.Sc. in Computer Science, Electrical Engineering, Data Science, Applied Mathematics, Physics, or a related technical field.
3+ years of hands-on research experience in machine learning and deep learning (in industry R&D or an academic research lab).
Deep theoretical and practical understanding of modern deep learning fundamentals (e.g., neural network architectures, optimization landscapes, generalization, attention/transformers, CNNs, or implicit representations).
Proven hands-on proficiency in Python and PyTorch, including writing custom layers, loss functions, and efficient training loops.
Demonstrated track record of creative problem-solving, proposing novel ideas, and executing them from hypothesis to experimental validation.
Strong technical communication skills and fluency in English

Preferred Qualifications
Track record of research contributions (e.g., publications in top-tier conferences like CVPR, ICCV, NeurIPS, ICLR, ICML, or relevant patents).
Foundational understanding or interest in physics, wave optics, signal processing, or computational imaging.
Experience with physics-informed neural networks (PINNs), neural operators (FNO/DeepONet), or integrating physical differential equations into loss functions.
Experience with large-scale distributed training, model profiling, and GPU acceleration.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8892453

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

01/10/2026

ML Systems Engineer

חברה חסויה

Location: Herzliya

Job Type: Full Time

We are looking for a Software Engineer specializing in ML Systems Engineering to join Finals Development department.

While vision and language models have become increasingly commoditized, Finals 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 Finals 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.

Requirements:
Requirements

B.Sc. with honors in CS/EE/Math/Physics, or a related field from a top-tier university.

5+ years of hands-on experience building and deploying large-scale deep learning systems in production.

Advanced proficiency in PyTorch/TensorFlow.

Preferred Qualifications :

M.Sc. or Ph.D. in a relevant quantitative field - Advantage.

Proficiency in Python/C/C++.

Deep, working knowledge of PyTorch internals.

Strong experience in several of the following areas:

Performance profiling and optimization of deep learning workloads.

Orchestrating and optimizing large-scale distributed training (hundreds to thousands of GPUs).

Optimizing model serving and inference pipelines (quantization, distillation, compilation, memory optimization, etc.).

Training and scaling state-of-the-art vision, language, or diffusion models.

Implementing custom CUDA/Triton kernels.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8838982

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

לפני 26 דקות

AI Research Engineer - Chip Design

חברה חסויה

Location: Herzliya and Haifa

Job Type: Full Time

As a member of our ML group, you will research and build AI systems that change how our silicon is designed. Many of these problems are still open: the right way to frame the problem isn't known yet, and the data is scarce or noisy.

We are looking for a strong researcher who enjoys working closely with design teams to turn open-ended questions into well-posed research problems, and who wants to see the results used in real projects. Success in this role means models and agents that measurably improve designed chip metrics (Power, Performance, and Area) and design turnaround. You will join a growing team of AI and software engineers working with our chip design teams.

Responsibilities
Research and develop AI methods for the physical design flow, from floorplanning and placement through routing and timing closure, combining predictive models, optimization, and generative AI.
Design and build agentic workflows that connect LLMs to EDA tools, design data, and internal infrastructure, to automate and speed up tasks such as flow setup, debug, design exploration, and analysis.
Work with design teams to turn open-ended problems into well-defined research questions with clear metrics, baselines, and success criteria.
Develop robust AI methods for limited, noisy, or imbalanced data, leveraging domain knowledge and advanced learning techniques.
Own data end to end: extract useful signal from EDA tool outputs and design databases, validate and clean it, and build datasets and benchmarks where none exist.
Run careful experiments and evaluations for both models and agents, and report clearly what works and what doesn't.
Work with software engineers and design teams to bring promising prototypes into production flows, and improve them based on feedback from real projects.
Help shape new research directions as our work grows, follow the AI and EDA literature, and bring relevant ideas to the team.

Requirements:
Minimum Qualifications
MSc with a research thesis, or PhD, in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
Research track record in which you led both problem formulation and execution, shown through peer-reviewed publications, a substantial thesis, or equivalent work.
Strong AI fundamentals and hands-on experience building, training, and evaluating deep learning models in Python (e.g., PyTorch).
Hands-on experience with generative AI, such as building and evaluating LLM-based applications or agentic workflows (e.g., tool calling, multi-agent orchestration, retrieval).
Experience getting reliable results from imperfect real-world data (small datasets, noisy labels, missing values, distribution shift), with sound judgment about when a result can be trusted.
Ability to work independently on loosely defined problems, and to explain technical results clearly to both AI specialists and hardware engineers.

Preferred Qualifications
PhD in AI, Computer Science, Electrical Engineering, or a related field
Publications at top-tier AI or EDA venues (e.g., NeurIPS, ICML, ICLR, DAC, ICCAD, MLCAD).
Familiarity with chip design flows such as physical design, place and route, and static timing analysis, or a strong interest in learning the domain in depth
Experience adapting foundation models (e.g., fine-tuning, distillation, preference optimization), building evaluation frameworks for agents, or deploying LLM systems in production.
Experience with one or more of: graph neural networks, Bayesian optimization, active learning, reinforcement learning, probabilistic modeling
Experience integrating AI models or agents into EDA tool flows through scripting (Python/TCL) or APIs.
Experience taking research into production, with solid software engineering practices.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8892480

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

לפני 1 שעות

Senior Software Engineer, Middleware - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

Somewhere between the physics of a sensor and the software people touch, there is a layer that decides whether a breakthrough actually works. That layer is yours. We're a small, truly multidisciplinary team that spans high-level software down to low-level physics, and we're building the foundation for our next big technology. As a Senior Software Engineer, you'll build the iOS- and macOS-based, low-latency application layer of our data collection and research system. It is the infrastructure that turns raw, real-world signals into the data that trains and validates the next line of our AI products. You'll write the code that makes it all work, and you'll help shape the applications built on top of it.
This is a rare chance to work on something new, from day one, alongside people who see the whole stack.

Responsibilities
Help create the next line of our AI products by building the infrastructure and laying the groundwork they will stand on.
Design and build low-latency middleware components for our-platform applications (iOS and macOS) supporting data collection and research workflows.
Own end-to-end implementation: architecture, coding, debugging, and performance tuning across the application layer.
Contribute to the design and implementation of downstream applications that consume the middleware (not just the plumbing - the product too).
Write robust, production-quality code in C++, Objective-C/C++, and/or Swift for performance-critical and system-level components, and Python for tooling, data processing, and automation.
Collaborate closely with firmware, data, and research teams to ensure the middleware correctly reflects real-world device and sensor behavior.
Use modern AI-assisted development tools as a core part of your workflow - for code generation, debugging, test authoring, and accelerating iteration.
Participate in design reviews, code reviews, and architectural decisions for the broader system.

Requirements:
Minimum Qualifications
B.Sc. in Computer Science, Electrical Engineering, or Computer Engineering.
5+ years of hands-on software development experience, with a track record of shipping production code.
Solid working experience in Python and in at least one of C++, Objective-C/C++, or Swift.
Comfort working across platforms and environments - shared code, build, and deployment.
Deep and broad programming fundamentals - comfortable moving across the stack from systems-level code to application logic.
Proven experience building low-latency or performance-sensitive applications (data pipelines, streaming, or similar).
Demonstrated ability to work effectively with advanced AI coding tools (e.g., Claude Code, Copilot, Cursor, or similar) as part of daily engineering practice.
A portfolio of delivered projects that demonstrate independent ownership and technical depth.
Strong communication skills and comfort working in a small, fast-moving team building novel systems.

Preferred Qualifications
Professional experience in all of the core languages: Python, C++, Objective-C/C++, and Swift.
macOS and/or iOS development experience - a rare and highly valued profile for this team.
Familiarity with sensor capture (video, audio) and multi-stream synchronization.
Familiarity with machine learning and inference (on-device or pipeline integration).
Experience with data collection/research tooling, instrumentation, or test-vehicle style software.
Background in embedded, firmware-adjacent, or hardware-integrated software.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8892438

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

29/10/2026

Machine Learning Ph.D Student - Agentic AI

חברה חסויה

Location: Herzliya

Job Type: Full Time

As a member of our ML group, you will design, prototype, and ship agentic GenAI systems used by chip design engineers to accelerate their day-to-day workflows. We are looking for a machine learning engineer who is energized by taking LLMs from "promising demo" to "depended-on tool": designing the agent loops, retrieval pipelines, and evaluation frameworks that make this transition real. Success in this role means agents that meaningfully shift how our customer teams work, backed by measurements you trust.

You will join a growing team of ML and software engineers building GenAI solutions for chip design teams. In this role, you will:

Design and build agentic systems featuring multi-agent coordination, long-horizon reasoning, tool use, and planning frameworks.
Develop end-to-end solutions, including domain-specific agents and high-performance retrieval pipelines spanning diverse data sources.
Partner with subject-matter experts on the customer teams to translate their workflows and rules of thumb into concrete agent assignments, prompts, tools, and guidelines.
Build the evaluation backbone for our agents: define success measures, curate offline golden sets, and instrument online telemetry to power feedback loops that drive fast, safe iteration with real cost and quality control.
Drive prompt engineering, model selection, and lightweight model adaptation as the workloads demand.
Collaborate closely with software engineers on the team to productionize your work, and with researchers and customers to shape what we build next.
Stay current with the rapidly evolving agentic and LLM landscape and bring promising techniques into our products with good engineering judgment.

Requirements:
Minimum Qualifications
MSc, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related fields.
Strong programming skills in Python and experience with modern ML and deep learning frameworks (e.g., PyTorch).
Hands-on experience with agentic frameworks, tool use, and data pipelines.
Strong evaluation mindset: comfortable designing golden sets, metrics, and telemetry to drive iteration.
Excellent communication and collaboration skills, with the ability to align with subject-matter experts and translate their domain knowledge into agent behavior.

Preferred Qualifications
Hands-on experience with stateful, multi-turn agentic frameworks and complex execution flows.
Experience with prompt engineering and optimization techniques.
Experience with model adaptation (e.g., fine-tuning, distillation, preference optimization).
Publications at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, or similar).
Familiarity with chip design or EDA environments.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8876935

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

07/10/2026

Embedded Engineer for the Silicon validation team

חברה חסויה

Location: Herzliya and Haifa

Job Type: Full Time

We're seeking a passionate and skilled Embedded Firmware Engineer to play a pivotal role in delivering the next generation of us. In this highly visible position, you'll be at the intersection of hardware and software, collaborating closely with logic designers to deeply understand system-level operations and performance requirements for hardware validation. In this role, you will develop critical low-level embedded firmware specifically designed to validate hardware functionality, ensure conformance, and drive performance during the bring-up and testing phases of new silicon. This involves designing, developing, and integrating complex drivers, diagnostic tests, and infrastructure essential for SoC bring-up, post-silicon validation, and debugging activities. Ultimately, you'll lead hands-on firmware development, system-level validation, and problem-solving at the hardware-software interface in a dynamic, collaborative environment, with your work directly enabling the successful launch of future products by ensuring our silicon meets the highest standards.

Requirements:
Minimum Qualifications
3+ years of experience in embedded firmware development within Real-Time (RT) or Embedded environments.
Strong proficiency in C, with a deep understanding of hardware-software interaction.
Hands-on expertise with Hardware-Software integrations, particularly for silicon validation and bring-up.
Exceptional debugging skills at the firmware and hardware level.
Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, or equivalent practical experience.

Preferred Qualifications
Experience with pre-silicon and post-silicon validation methodologies.
Familiarity with CPU architectures (e.g., ARM) and low-level system programming.
Direct experience with System-on-Chip (SoC) development or validation.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8847754

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

לפני 20 דקות

Physics & Optical Simulation Software Engineer - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

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.

Requirements:
Minimum Qualifications
M.Sc. in Computer Science, Software Engineering, Applied Mathematics, Electrical Engineering, Physics, or a related technical field.
3+ years of professional software development experience focused on building large-scale simulations, scientific computing pipelines, or data-intensive software platforms.
Advanced proficiency in Python, including object-oriented design, performance profiling, and modern package development.
Demonstrated track record of independently learning and mastering unfamiliar technical or scientific domains to deliver innovative solutions.
Strong problem-solving skills and experience architecting large-scale, automated software workflows.
Strong cross-functional collaboration and communication skills in English.

Preferred Qualifications
Domain background or foundational understanding of wave optics, ray tracing, electromagnetics, or physical modeling.
Experience with scientific computing libraries and numerical frameworks (e.g., NumPy, SciPy, Numba, CuPy, JAX, or PyTorch).
Experience designing distributed computing workflows, batch simulation schedulers, or cluster/cloud-based execution engines.
Familiarity with C/C++ integration (e.g., pybind11, Cython) or accelerating simulation kernels via GPU/multithreading.
Experience interfacing with lab measurement infrastructure and validating simulation models against physical laboratory prototypes.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8892486

שירות זה פתוח ללקוחות VIP בלבד

שמך המלאמה השם שלך?

מייל

תיאור

שליחה

תודה על שיתוף הפעולה

מודים לך שלקחת חלק בשיפור התוכן שלנו :)

המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

לפני 57 דקות

Privacy-Preserving Machine Learning Researcher - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

We are looking for a research scientist with an extraordinary theoretical background in machine learning and computational mathematics. This is a foundational theory role: we are seeking someone who truly understands how learning algorithms function from first principles, identifies computational and statistical barriers, and proves what is achievable under information-theoretic constraints.
While knowledge of privacy mechanisms (such as Differential Privacy) and cryptography is a strong advantage, the core priority is deep theoretical maturity in statistical learning, sample complexity, and optimization theory. You will bridge mathematical rigor with practical algorithmic innovation, shaping the next generation of our private machine learning.

Responsibilities
Conduct foundational theoretical research into the limits, bounds, and trade-offs of learning algorithms under privacy, computational, and statistical constraints.
Formulate and prove theoretical guarantees for machine learning models, analyzing sample complexity, generalization error, and convergence rates.
Design, analyze, and benchmark novel privacy-preserving learning algorithms (e.g., DP-SGD, private aggregation, and local/central differential privacy paradigms).
Collaborate with applied research and engineering teams to translate theoretical insights and mathematical proofs into practical, scalable machine learning solutions.
Explore intersections between statistical learning theory, optimization landscapes, and cryptographic primitives.
Author high-impact research papers for publication in top-tier machine learning and theoretical computer science conferences.
Stay at the forefront of foundational ML theory and privacy literature, identifying theoretical bottlenecks and proposing novel avenues of investigation.

Requirements:
Minimum Qualifications
Ph.D. in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a related theoretical/quantitative field.
Deep theoretical foundation in statistical learning theory, computational complexity, and optimization (e.g., sample complexity, PAC learning, empirical process theory, or convex/non-convex optimization).
Proven track record of high-impact theoretical research and mathematical proof generation, demonstrated by publications in top-tier venues (e.g., COLT, NeurIPS, ICML, ICLR, STOC, FOCS, or IEEE TIT).
Strong mathematical intuition and the ability to rigorously analyze the theoretical behavior and fundamental limits of algorithms.
Hands-on ability to prototype, validate, and simulate algorithmic concepts using Python.
Excellent written and verbal communication skills in English, with the ability to convey complex mathematical theory to both theoretical peers and applied teams.

Preferred Qualifications
Domain background or prior research in Differential Privacy (DP), DP-SGD, private PAC learning, or privacy budget accounting.
Familiarity with cryptographic concepts and protocols (e.g., Secure Multi-Party Computation / MPC, Zero-Knowledge Proofs / ZKP, or Homomorphic Encryption).
Experience bridging theoretical bounds with empirical deep learning models and large-scale training dynamics.
Understanding of information theory, concentration of measure, or high-dimensional statistics.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8892442

שירות זה פתוח ללקוחות VIP בלבד