דרושים » הנדסה » Data & Signal Analysis Scientist - Q

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.

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.

We are an innovative, growing physics and engineering team working at the intersection of breakthrough scientific exploration and real-world product feasibility. We are seeking an exceptional Scientist, specializing in optics and non linear optics, to lead the conceptualization, design, and experimental validation of novel and unique optical architectures. In this role, you will investigate uncharted concepts, explore first-of-their-kind ideas, and turn exploratory physics into viable system-level solutions with the potential to reshape technology.

Explore, architect, and validate early-stage optical concepts and architectures where final specifications and solutions are yet to be defined
Design, model, and simulate advanced optical systems, focusing on ultrafast laser optics, nonlinear optical phenomena, and optical scanning mechanisms
Build, align, and operate experimental lab setups to prototype ideas, evaluate feasibility, and characterize novel optical phenomena
Partner with external vendors and specialized component suppliers from initial technical engagement through fabrication, testing, and delivery
Investigate physical limitations, identify fundamental trade-offs, and define requirements for next-generation systems
Translate complex experimental and theoretical findings into clear engineering insights for multidisciplinary hardware, software, and systems teams.

Responsibilities
Lead optical concept exploration: de-risk high-uncertainty ideas through rigorous modeling and hands-on lab experimentation
Design and implement optical systems involving ultrafast lasers, nonlinear optics, and beam-scanning systems
Establish and drive external vendor relationships, including optical specification drafting, design reviews, and vendor deliverables qualification
Develop custom experimental benches and test procedures to validate system-level performance against physical limits
Analyze complex experimental datasets and communicate actionable recommendations to cross-functional engineering teams

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.

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.

Play a part in shaping the future of human-computer interaction. We're looking for an Engineering Program Manager (EPM) to turn advanced machine learning in speech, audio and computer vision into features that ship to customers.
You'll work across ML research, data collection, silicon, FW, and the device and companion SW teams that bring these features to our customers. Your work will cover every stage, from the first dataset to the final release.

You'll own the plan for taking ML features from research to release. That covers data planning, model training and evaluation, optimization for our silicon, integration with SW teams, and release. You'll keep teams aligned, make risk visible early, and help leadership decide when model quality, performance and schedule pull in different directions. You'll be comfortable with how uncertain research is, and disciplined about delivering what ships.

Responsibilities
Own the delivery plan for ML features, from research and prototyping through integration, validation and release.
Partner with data teams to build the data strategy and plan, making sure it covers what the models need and supports program milestones: what data, at what scale and coverage, and by when.
Align model milestones with HW builds, pre-silicon validation (simulation and FPGA), and silicon bring-up, ensuring models are ready at each gate.
Lead integration with SW groups, including model handoff, interfaces, integration schedules and release timing.
Align ML teams, MLOps and data science on priorities, compute capacity and dependencies.
Track model quality, latency, memory and power against product targets, and lead trade-off decisions.
Set up model release practices: readiness gates, versioning, regression tracking and post-integration monitoring.
Summarize ML progress into clear, data-driven recommendations for leadership.

The Cellular group is looking for an extraordinary Cellular-RF Post-Silicon Validation Engineer. In this role you will be responsible for developing and executing comprehensive RF validation strategies for cellular silicon products, conducting meticulous RF performance, analyzing complex measurement data and collaborating with cross-functional teams to identify and resolve silicon design issues. You will leverage your deep understanding of RF system parameters, measurement methodologies and cellular standards to ensure products meet rigorous quality and performance specifications throughout the development lifecycle.

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.

We are looking for a Privacy Preserving Engineer who combines strong machine learning fundamentals with practical software engineering skills. This is an applied, hands-on engineering role: you will work closely with theoretical researchers to deeply understand their mathematical formulations, understand the core contributions of their work, and modify the heart of our training pipelines and models accordingly.

Responsibilities
Translate advanced mathematical theory, privacy mechanisms, and research formulations into robust, practical machine learning systems.
Dive deep into core model training loops (in PyTorch) to modify, inject, and optimize custom components, loss functions, clipping mechanisms, and private optimizers.
Design, architect, and implement end-to-end privacy-preserving machine learning pipelines.
Evaluate privacy-utility trade-offs, measuring model accuracy, convergence behavior, and empirical privacy leakage.
Collaborate closely with theoretical research scientists to validate algorithmic hypotheses and verify theoretical guarantees in practical setups.
Package, document, and present research implementations, technical prototypes, and proof-of-concepts clearly to cross-functional teams and leadership
Stay updated o.n modern privacy-enhancing technologies and open-source differential privacy tooling.