דרושים » הנדסה » Engineering Program Manager - Machine Learning Features

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 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 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.

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.

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.

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'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.

We are looking for a Software Engineering Manager to lead the SW Tools & Automation team in our Cellular group in Herzliya. The team builds and runs the engineering backbone of Cellular RF validation. You will be in charge on a variety of domains: Validation environments, CI/CD and lab automation infrastructure, the data path (lab results), & a broad set of tools for test execution, results analysis and lab monitoring.
As manager, you will own the team's technical vision and its people. You will turn the needs of many engineering teams into a clear roadmap and make sure what we deliver is reliable, scalable and actually used.
You will inherit an established, high-impact team with a broad, mature portfolio, backed by a world-class organization. You will be able to shape the direction of systems used across our cellular validation, work with talented colleagues in Israel and around the world, and grow your own career within us.