דרושים » AI » DSP and AI libraries Software Team Leader

As a Graph Compiler Software Manager, you will lead the development of next-generation graph compiler technology. This compiler is a cornerstone of AI software offering, empowering customers to optimize, map, and execute complex neural networks across architectures with extraordinary efficiency. You will lead a group of talented engineers and team leaders working across domains such as compiler optimizations, graph-level transformations, operator mapping, scheduling, and code generation. We are seeking a visionary and technically strong leader with passion for excellence and impact in one of the most fascinating areas of AI and deep learning.

Key Responsibilities:

Lead the design and implementation of innovative graph compilation flows for AI workloads, from high-level optimizations to hardware-specific backends. Build and guide a strong, innovative, and results-driven compiler team that thrives on solving complex technical challenges. Collaborate with hardware architecture, VLSI, and software framework teams worldwide to ensure seamless integration and maximum performance. Lead the end-to-end delivery of graph compiler components while meeting aggressive KPIs. Establish and execute group planning processes to ensure timely and high-quality delivery. Provide technical and managerial guidance to team leaders and engineers. Define and drive compiler optimization strategies for AI and deep learning workloads.

In this role, you will be a key contributor to the design and implementation of AI Graph Compiler software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.

You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of NPU embedded solutions, actively promoting AI capabilities to the customers.

What will you do:

Own and design key components of the AI Graph Compiler software stack for NPU-based systems.

Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.

Collaborate on HW-SW co-design, influencing NPU architecture.

Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.

Mentor junior engineers and contribute to technical best practices.

seeking a hands-on Embedded Software Technical Lead to play a key role in the design and development of firmware for our next-generation rider products. This role is focused on deep technical ownership, system -level thinking, and high-quality implementation, and is ideal for an experienced Embedded engineer with a strong algorithmic background and solid architectural understanding-who can also mentor and lead a small team of 2-3 Embedded engineers while remaining highly hands-on in day-to-day development. What Youll Do
* Take hands-on responsibility for the design and implementation of Embedded software architecture for new wireless communication devices (ARM Cortex-M, RTOS, ultra-low-power systems).
* Design, implement, and optimize complex Embedded algorithms, including Real-Time control flows, communication logic, and audio/ DSP -related pipelines.
* Write, review, and maintain high-quality, production-grade C / C ++ code.
* Contribute to system -level architectural decisions and ensure scalable, robust firmware design.
* Define and promote coding standards, design patterns, and best practices across the firmware domain.
* Act as a technical focal point for debugging, root-cause analysis, and performance optimization of complex issues.
* Lead a small team of 2-3 programmers/engineers: provide technical direction, help with task breakdown and prioritization, and ensure high-quality delivery.
* Mentor engineers through design reviews, technical discussions, pairing, and hands-on collaboration-raising the teams engineering bar.
* Work closely with Hardware, QA, Mobile App, Cloud, and Product teams to deliver integrated, end-to-end solutions.
* Participate in technical roadmap discussions and evaluation of tools, technologies, and wireless stacks.

In this role, as Embedded Software Team Leader, you will lead a software team taking major part in developing AI framework. This framework is at the heart of the AI software offering, with deep impact on multiple hardware and software teams ability to execute state of the art LLM, Generative AI and Neural Networks with extraordinary performance. You will lead a team of creative and result oriented enthusiasts that invent, design, and implement software that enables smooth connectivity between the NPU and Host CPU.

Key Responsibilities:

Lead and manage the Embedded Software engineering team, ensuring high performance and productivity. Oversee the design, development, and implementation. Collaborate with cross-functional teams to integrate solutions. Provide technical guidance and mentorship to team members. Ensure project timelines and deliverables are met. Conduct performance reviews and support professional development of team members. Collaborate globally with architects, HW teams, NPU teams, and other developer teams.

The AI Graph Compiler Engineer will design and develop next-generation graph compiler technologies enabling efficient execution of advanced AI models on Neural Processing Units (NPUs) used in edge and embedded AI devices. In this role, you will work at the intersection of AI frameworks, graph optimization, and hardware acceleration, enabling efficient execution of neural networks on cutting-edge AI hardware.

You will work at the intersection of AI frameworks, compiler infrastructure, and hardware acceleration, helping translate high-level AI models into highly optimized executions on NPUs.



What will you do:

Develop and enhance AI graph compiler components targeting NPU architectures. Implement graph-level optimizations such as operator fusion, scheduling, memory planning, and layout transformations. Participate in lowering models from AI frameworks (e.g., PyTorch, ONNX, TensorFlow) into NPU-optimized representations. Contribute to compiler passes focused on performance, memory efficiency, and numerical correctness. Collaborate closely with hardware, runtime, and AI framework teams to achieve optimal end-to-end performance. Analyze performance bottlenecks and assist in compiler-based optimizations. Debug and resolve issues across compiler, runtime, and hardware layers. Support testing, validation, and documentation of compiler features.

Own and design key components of the Edge AI software stack for NPU-based systems.

Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.

Collaborate on HW-SW co-design, influencing NPU architecture.

Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.

Mentor junior engineers and contribute to technical best practices.

You will participate in implementing a new strategic platform offering Generative AI (Gen AI) capabilities such as inference, fine-tuning, data curation, data labeling, and more.

We are seeking a highly skilled Gen AI software engineer to design, develop, and deploy state-of-the-art, scalable, efficient Gen AI solutions. You will be part of a high-performing engineering team that values collaboration, innovation, and teamwork. This role is perfect for someone who thrives in an environment where ideas are shared, challenges are met together, and success is a collective achievement.

Responsibilities:
Designed and implemented Gen AI platform components using Python and modern microservices.
Drive engineering and operational excellence and best practices.
Collaborate with tech, product, program, and business peers to deliver customer value and drive product vision.
Drive the end-to-end development of AI systems, from research and experimentation to deployment and scaling.
Mentor and support engineers, fostering a culture of collaboration, knowledge-sharing, and continuous learning.
Analyze complex problems and identify and define the requirements to solve them.
Apply DevOps principles to maintain high-performance and scalable AI systems.
Design and implement secure APIs, ensuring data protection and safe interaction with AI models.
Stay updated on the latest Gen AI trends and technologies, bringing fresh ideas to the team.

We are looking for a Senior BLE Testing Automation Developer to join our Bluetooth Low Energy (BLE) system testing team. The team designs and develops a wide range of capabilities to support BLE system-level validation, including internal tools, end-to-end test systems, automation libraries, core infrastructure, and comprehensive test plans. These systems simulate real-world customer scenarios and support validation against BT SIG specifications.
In this role, you will design and develop new test plans and test cases for our BLE connectivity solutions, maintain lab infrastructure, and support validation activities across multiple projects and device families. You will analyze customer requirements, use cases, scenarios, and reported issues to ensure proper testing coverage, and validate that the system is compliance with BT SIG specifications and customer requirements. Your responsibilities will include design and integrating new BLE features testing into automated workflows, developing advanced test automation sequences, monitoring and generating test reports, design and reviewing test plans, and mentoring junior team members. This role requires proven test automation experience with embedded systems, strong technical problem-solving skills, collaborating with other software teams as needed, and may involve international travel for customer support.

We are looking for an AI & Automation Engineer to design, build, and enable enterprise AI infrastructure and automation solutions.
This role focuses on AI architecture, workflow automation across IT and business applications, and monitoring, governance, and cost management of AI tools and agents.
You will work at the intersection of AI platforms, IT operations, and business automation, enabling scalable, secure, and cost-effective AI adoption across the organization.
Responsibilities:
Select and integrate AI tools, frameworks, and platforms aligned with enterprise IT standards.
Ensure scalability, security, reliability, and compliance of AI infrastructure.
Enable AI solutions from concept to production, including:
AI-powered IT tools
AI agents and copilots
AI services integrated into business applications
Collaborate with IT, DevOps, Security, and Business teams to deploy AI solutions smoothly.
Build reusable AI enablement patterns, templates, and best practices.
Automation of Workflows
Design and implement automation of IT and business workflows using AI and orchestration tools.
Integrate AI agents into:
IT operations (ITSM, monitoring, incident response)
Business applications and internal systems
Improve efficiency, reliability, and user experience through intelligent automation.
AI Monitoring, Usage & Financial Governance
Define and implement monitoring for AI systems, including:
Usage tracking
Performance and reliability
Model and agent behavior
Track and analyze financial usage and cost of AI tools, APIs, and platforms.
Establish governance frameworks for responsible and controlled AI usage.

A new type of finance role designed for an AI-driven organization. You will combine strong FP&A fundamentals with curiosity for GenAI tools, modern analytics, and SaaS monetization. Working closely with AI leaders, product teams, and business stakeholders, you will help translate innovation into financial insights while contributing to core financial execution.

What will your job look like?
Support planning, forecasting, and performance analysis for Data & GenAI initiatives
Build financial models for AI solutions, SaaS offerings, and data-driven business cases
Analyze monetization approaches for GenAI-driven capabilities and internal transformation programs
Design and track KPIs measuring efficiency, productivity, and value from AI adoption
Partner closely with product, engineering, and AI leaders to translate roadmaps into financial narratives
Use GenAI and analytics tools to streamline reporting and improve decision-making workflows
Maintain ownership of core FP&A deliverables while supporting strategic decision-making