לוח משרות דרושים בוגרי מדעי המחשב משרה מלאה, לחיפוש זה נמצאו 75 משרות

Our team is looking for an FW engineer to design, develop, and implement complex SW modules for the Apple production line. The integration will require cooperation with other teams and vendors abroad. Youll be part of a team developing complex and versatile hardware/software integrated solutions for mission-critical tasks in future Apple products.
Are you a big-picture thinker who loves setting ambitious goals?
Do you have a passion for understanding how each line of code affects all the others?
Your dedication to cross-disciplinary collaboration will help develop groundbreaking technologies like iOS, macOS, watchOS, and tvOS. By crafting these distinct, holistic user experiences, youll continue to uphold and advance the excellence people expect from Apple devices.

Take part in SW design sessions (detailed designs involving system understanding), provide HLD and LLD documentation of central SW modules, participate in the coding of complex SW modules, debug complicated (system-wide) bugs, and lead HW-SW integrations.

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.

We're looking for an Engineering Program Manager (EPM) to lead a multidisciplinary program that takes a new sensing technology from research to product. The program covers HW, sensing, mechanical engineering, FW, SW, and ML. You will turn early research into a roadmap that can ship, and keep the teams aligned around one goal: a new kind of interaction experience for our users.
This is not a typical execution role. You'll work closely with researchers and engineers while driving and supporting execution. You'll know when to give exploration room and when to push toward productization. You'll be fluent in both scientific uncertainty and product discipline.

You'll be the central point of coordination for a program that moves through research, feasibility, prototyping, and product development at the same time. Working with world-class scientists, engineers, and ML experts in Israel and the US, you'll define the plan, make technical risk visible, and help leadership make clear decisions when the path forward isn't obvious. Along with engineering leadership, you'll be the face of the program.

Responsibilities
Build and own the program roadmap, from research milestones and feasibility checkpoints through prototype builds and productization.
Turn open-ended research questions into structured plans with clear hypotheses, success criteria, and decision gates.
Summarize research results into clear, data-driven findings and recommendations for executive audiences.
Keep a collaborative, transparent culture that respects research rigor and product urgency equally.
Align cross-functional teams across HW, physics, optics, mechanical, FW, ML/algorithms, data collection, and user experience.
Manage dependencies between ML workstreams, data collection, and HW development so learning cycles stay fast.
Run prototype and development builds with engineering teams, operations partners, and suppliers.
Spot technical and organizational risks early, and present them as strategic choices with mitigation paths.
Keep a collaborative, transparent culture that respects research rigor and product urgency equally.
Improve program methods to fit the needs of a research-to-product program.

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.

As a RTL Engineer, you will own or participate in the following:
* Microarchitecture development and specification - from early high-level architectural exploration through micro architectural research and arriving at a detailed specification.
* RTL feature ownership - development, assessment and refinement of new RTL features to target power, performance, area and timing goals.
* Validation - support test bench development and simulation for functional and performance verification.
* Performance exploration and correlation - explore high performance strategies and validate that the RTL design meets targeted performance.
* Design delivery - work with multifunctional engineering team to implement and validate physical design on the aspects of timing, area, reliability, testability, and power.

Responsibilities
Help create the next line of our AI products by leading the build of the infrastructure and groundwork they will stand on.
Lead the architecture and delivery of the low-latency middleware layer behind our iOS and macOS data collection and research applications.
Stay hands-on: write code, review pull requests, and debug the hard problems alongside the team. This is not a purely managerial role.
Set technical direction and coding standards across C++, Objective-C/C++, Swift, and Python.
Own the roadmap with research, firmware, silicon, and product partners, and turn ambiguous, ambitious goals into a concrete technical plan.
Drive design and implementation decisions for the applications on top of the middleware, not just the layer underneath.
Mentor and grow a team of strong engineers, and run the design and code reviews that set the culture.
Bring AI-assisted development into the teams daily workflow and show how much a small team can ship.
Work across the whole stack, from high-level software to low-level physics, and connect the disciplines that make it work.

Were looking for a software developer with a real passion for hardware. You enjoy the moment when code makes a physical device do something, youre comfortable standing at a lab bench, and you take a problem from a vague request to a working solution that people actually use.

Responsibilities
Design and build macOS tools and applications, primarily in Python and Swift, for hardware test setups, lab research setups, and lab automation.
Develop company-wide tools used by multiple teams for dedicated proprietary hardware research, and support them as real products with real users.
Own problems end to end: understand the need with the people at the bench, design the solution, implement, deploy, and iterate until it works in the lab.
Integrate with instruments, sensors, and custom hardware over USB, serial, Bluetooth, network, and other lab interfaces.
Build supporting infrastructure as needed: cloud dashboards, databases, data pipelines, and reporting for test and research results.
Implement algorithms and data-processing logic that turn raw measurements into usable results.
Support Windows environments when lab equipment or vendor software requires it.
Make advanced AI coding tools a core part of how you build, and help the team move faster with them.
Take part in design and code reviews, and help raise the quality and maintainability of the teams tools.

at the company, were reinventing devsecops to help the worlds greatest companies innovate. our team of industry-leading software security experts is a true pioneer, constantly pushing the boundaries with original research and technological innovation. we are a special place with a unique combination of brilliance, spirit, and just all-around great people. thousands of customers, including the majority of the fortune 100, trust the company to manage, accelerate, and secure their software delivery from code to production - a concept we call liquid software. wouldn't it be amazing if you could join us on our journey?
we're looking for an experienced data Scientist to join our ds team under the CIO office. the key responsibilities are to provide our internal business partners with robust, reliable, and innovative ai / ml / ds solutions for their business problems, providing direct value to the business. an ideal candidate blends solid data science foundations, proven backend engineering skills, and a demonstrated track record building real-world systems. you should be able to take an idea (or propose one) and independently work from poc to production mvp, know when to move fast and when to slow down, while continuously communicating with stakeholders as their trusted partner, and enjoy collaborating with members of the team.
as a data Scientist at the company you will...
listen to our business stakeholders and their challenges and propose data -driven solutions to problems using ml models and advanced statistical methods. for example, a classifier for a rare event that will trigger business action, or a time series predictor in a noisy environment that will affect business planning.
work side-by-side with business units to gather requirements, collaborate with cross-functional teams, iterate on the process of data research, and deliver to production.
own ml models full lifecycle and monitor existing models, while continuously improving our automation pipeline from a ds perspective.
rapidly prototype new ideas (e.g., custom rag-based solutions, agentic processes, knowledge graphs) based on applied research or academic concepts, and if successful, productionize.
be a primary contributor for the semantic engine component of the organizational brain platform and be able to serve as a technical authority in that domain.
look for improvement and innovation opportunities with business impact, and be a source of high-quality ideas.
be an independent contributor with excellent communication skills, both in writing and verbally.

As a Camera System Engineering EPM, you will:
Own and drive the execution of next-generation camera system programs across our product lines.
Collaborate with electrical, mechanical, optical, and software teams to deliver seamless and innovative imaging solutions.
Navigate ambiguity, solve complex system integration challenges, and lead programs from concept through launch.
Influence product and technology roadmaps by working closely with design, architecture, and operations teams.
Be the voice of the program-communicating milestones, risks, and vision clearly across executive and engineering leadership.
Thrive in a high-energy environment where every day brings a new challenge and a new opportunity to innovate.

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.

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 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.

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 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.

Our growing team is looking for a team player with versatile technical skills. You will engage in a cross-functional, collaborative environment that will take your debugging and technical skills to the limit while integrating cutting-edge technologies into our products.
In this role, youll be responsible for bringing groundbreaking wireless connectivity to the world through Wi-Fi, Bluetooth, and more. You will collaborate across departments to help craft wireless solutions that elevate the customer experience with our products and services.

In this role, you will be responsible for:
Work as part of a team to operate a large-scale test automation lab for our multiple products.
Test and integrate SW and HW deliveries from various global R&D teams, including managing, debugging, and failure analysis of automated tests.
Automate manual tasks for the lab and collaborate with technology teams to migrate more setups to the lab.
Constantly improve and innovate our lab procedures, HW setups, and SW tools.
This is a fixed-term position, with the possibility of extension.