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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

Were looking for a Staff AI Engineer to join our growing AI and engineering group. This is a highly impactful role for someone who wants to shape how AI is embedded across products, platforms, and business operations-from early experimentation to production-scale systems.As a Staff AI Engineer, youll operate end-to-end: identifying opportunities, designing solutions, and delivering real business value using modern AI and LLM-based systems.

What you'll be doing
Design, build, and deploy AI-powered solutions across Supersonics products and internal platforms
Partner closely with product, data, engineering, and business stakeholders to identify and execute high-impact AI use cases
Architect and implement agentic workflows using frameworks such as LangChain, LangGraph, or equivalent
Integrate and operate LLMs via SDKs, including prompt design, structured outputs, tool/function calling, and guardrails
Lead technical decisions around AI evaluation, monitoring, observability, and quality measurement
Own AI initiatives from concept through production, including iteration, scaling, and long-term maintenance
Stay up to date with emerging AI technologies, models, and best practices, and help bring them into production thoughtfully

We are Looking for a production analyst to join our Technical Support team.
As a Production analyst in the Support team, you'll collaborate with the business team, R&D team, and BI team and dive into the heart of our platform, analyzing large data sets to address technical production incidents.

What you'll be doing
Conduct in-depth analysis of vast and intricate data sets, reflecting the behaviors of millions of users, to swiftly address technical production incidents.
Take charge of the resolution process for cases, collaborating seamlessly with internal teams, and maintaining transparent communication with stakeholders throughout.
Develop internal data tools for the Technical Support team to improve the investigation capabilities of the team.
Proactively contribute to internal documentation and promote knowledge sharing within the team.
Effectively manage and prioritize multiple customer issues across our range of products and platform

Technical Leadership & Architecture: Drive data infrastructure strategy and establish standardized patterns for AI/ML workloads, with direct influence on architectural decisions across data and engineering teams
DataOps Excellence: Create seamless developer experience through self-service capabilities while significantly improving data engineer productivity and pipeline reliability metrics
Cross-Functional Innovation: Lead collaboration between DevOps, Data Engineering, and ML Operations teams to unify our approach to infrastructure as code and orchestration platforms
Technology Breadth & Growth: Work across the full DataOps spectrum from pipeline orchestration to AI/ML infrastructure, with clear advancement opportunities as a senior infrastructure engineer
Strategic Business Impact: Build scalable analytics capabilities that provide direct line of sight between your infrastructure work and business outcomes through reliable, cutting-edge data solutions
What you'll be doing
Design Data-Native Cloud Solutions - Design and implement scalable data infrastructure across multiple environments using Kubernetes, orchestration platforms, and IaC to power our AI, ML, and analytics ecosystem
Define DataOps Technical Strategy - Shape the technical vision and roadmap for our data infrastructure capabilities, aligning DevOps, Data Engineering, and ML teams around common patterns and practices
Accelerate Data Engineer Experience - Spearhead improvements to data pipeline deployment, monitoring tools, and self-service capabilities that empower data teams to deliver insights faster with higher reliability
Engineer Robust Data Platforms - Build and optimize infrastructure that supports diverse data workloads from real-time streaming to batch processing, ensuring performance and cost-effectiveness for critical analytics systems
Drive DataOps Excellence - Collaborate with engineering leaders across data teams, champion modern infrastructure practices, and mentor team members to elevate how we build, deploy, and operate data systems at scale

Were seeking a motivated, detail-oriented Front-End Engineer to craft seamless, high-performance experiences across browsers and WebViews , youll work in a dynamic environment that brings together web front ends, backend services, and machine learning into a unified, complex system. This is an ideal role for engineers eager to make a substantial impact on a technically challenging product in a fast-paced setting.
What you'll be doing
Build and maintain React-based SPAs and NX-powered micro frontends
Implement high-quality TypeScript, HTML, and CSS
Translate Figma designs into reusable, responsive components and design systems
Optimize for WebView and multiple browsers, ensuring cross-platform consistency
Collaborate with product/design to iterate quickly and ship value

Were looking for a Junior Developer Support Engineer to join our growing team in Tel Aviv. In this role, youll become an expert in our products and serve as a key technical point of contact for both customers and internal teams. Youll have a direct impact on customer satisfaction and product quality by resolving complex issues and working closely with R&D, Product, and Business teams.
This is an opportunity to deepen your technical knowledge, build cross-functional partnerships, and drive meaningful improvements in both user experience and internal processes. Join us and be part of a team where your ideas, initiative, and technical skills will make a real difference.

What you'll be doing
Investigate complex technical issues and identify root causes and solutions using diagnostic tools.
Provide high-level technical support to strategic customers and internal teams.
Collaborate closely with R&D and Product teams to escalate and resolve issues and improve product functionality.
Create and maintain internal documentation and knowledge base articles.
Deliver technical training to other teams and support cross-functional projects.

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.

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.

Lead and mentor a team of software engineers developing Sensing AI SDK. Architect and implement high-performance sensing SW components for AI, vision, Audio and voice optimized for vectorize SIMD execution. Collaborate with compiler and DevOps teams to ensure efficient development and deployment. Drive technical excellence through code reviews, design discussions, and continuous improvement. Own the SDK roadmap, planning, and delivery cycles. Engage with customers to enable successful pre-sale processes, support integration and gather feedback.

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 managing the MBBU projects involving multiple domains including: VLSI architecture, VLSI Design, VLSI verification, VLSI backend, Algorithms and Software. These projects include development of products like cellular modems, Communication Digital Signal Processors and Radar/Lidar baseband with a total of up to 100 R&D engineers involved.

This role is reporting to the MBBU General Manager.

Responsibilities

Full ownership on all R&D projects of the Mobile Business Unit, including Software, VLSI and Algorithms tasks.

Direct management for the PMO

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.