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This is a crucial role on a small, senior team. You own your work end to end: UX & UI in one person. No one handing you specs, no layers between you and the product or engineering. You'll work directly with our VP of Product on a product that doesn't yet have a design playbook. That last part is the real job. We're designing a category that doesn't exist. Surfaces where both people and AI agents consume the same information. Interfaces for multiple types of personas - technical teams who live in APIs and business owners who care about business output. Views that shift by role and permission. Nobody has good patterns for this. You'll invent them.


What you'll do
Own product design end to end, from core experiences to the details nobody else notices.
Take an abstract, ambiguous problem and turn it into something people trust on sight.
Design for two users at once: the human making a decision and the agent doing the work.
Make governance, provenance, and permissions feel like clarity, not compliance overhead.
Shape the brand and make us feel like nothing else in the category.

Required Senior Software Engineer, EDACloud
About the job
Our Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to our Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The EDACloud team was established to provide tools and support for our rapidly expanding hardware engineering teams. We develop and manage unique software for deploying industry-standard Electronic Design Automation (EDA) tools on Cloud. As a Software Engineer, you will be focused on developing automation, including backend Remote Procedure Call (RPC) services, for seamless tool deployment. Additionally, we're developing cloud-based solutions to scale these workflows effectively, and generate unique insights for our users using advanced machine learning techniques. This critical environment directly impacts hardware creation.
Responsibilities
Design and deploy AI-driven strategies to streamline routine operations and power strategic programs.
Design, implement, and support the transition of custom chip design environments onto standardized, cloud-hosted virtual developer workstations.
Design and implement highly available, distributed services from the ground up, moving beyond automation scripts to robust, production-grade microservices.
Help define the technical roadmap for the teams service stack, evaluating new technologies and cloud-native tools to improve future scalability and performance.
Identify and refactor technical debt within the codebase to improve modularity, maintainability, and code quality.

We are looking for a Nitro PM & Ops lead to be part of K2 product line leadership. This role will participate in defining, shaping and manufacturing solutions to next generation of our cloud platforms. Specifically, this role will handle: Capacity, build, allocations (NPI), cost, NRE, planning and risk buys, strategy.

Key job responsibilities
Own K2 Design Partners (Joint Development and Manufacturer) and Cotract Manufacturers (CM) Engagement: Build a (3Y-to-5Y) strategic plan to allow development of multiples card SKUs (3-5) per year using design partners (JDM). The plan should optimize the design capabilities (to deliver best-in-class product, including advanced technologies), and availability (to meet out tight timeline); while providing alternatives to Engineering. This include also:
- Own K2 Budget related to Engagements: planning, handling the RFP process and Awards; and handling payments and budget.
- Maintain a strategy discussion forum, collecting inputs from all stake holders.
Maintain K2-to-Vendor relationship: whether these are JDMs, CMs, or other components/sub-components/services Vendor related to K2 - maintain the relationship w their leadership, identify opportunities and pains. Propose actions to K2 and K2 Supply leadership. This includes also:
- Vendor management: own vendor engineering logistics, samples management (order/allocation), monitoring, actual on-ground visits to monitor health, 2nd source components needs mapping (into the NPI team), vendor qual planning (cards per vendors).
- Propose solutions to Vendor's pains: working closely with K2 supply org.
- Integrate Design and Q&R inputs to a single message: working w AP K2 Q&R team.
Own the cooperation with K2 Supply Chain (AIS): work w K2-Supply (AIS) leadership to build and improve methodlogy, targeting maintiaing supply health. Handle Supply issues related to unexpected shortages (greater than inventories) and engineering issues. This includes also:
- CM selection strategy - build scorecard; provide feedback to vendors/design-partners.
- Provide inputs in K2 Supply Planning: be the engineering bar raiser to K2 supply plan (owned by K2 planner at AIS)
Own K2 Program Management: guide K2 Program Manager, to own cross cards (per Gen) items. This includes:
- Drive shared technologies (Ethernet, PCIe, DRAM) integration plan. Work w partner Org in AWS (eg NPD, AIS, HW-Eng) on per generation plan with milestones. This while getting definitions from K2 owner, and Ethernet, PCIe and DRAM owners.
- Own Readiness to new Chip bring up: Labs plan, Dev systems needs & deliveries plan management, manage priorities across activities in AP and at the ECO systems (peer teams) outside AP.

Required Founding Product Designer
About us:
We transform trade contractors' knowledge, documents, and workflows into AI-powered systems. We're hiring our first product designer to set the design direction for the whole platform - every product surface, every agent interaction, every moment a customer trusts the software to make a decision.
Key Responsibilities:
Own the design system and the product's overall visual and interaction language
Design AI-native interactions: citation chips, source previews, streaming responses, confirmation flows
Partner with engineering to ship high-craft features that hold up to real use on a jobsite
Build a research practice grounded in real customer time, not lab studies
Set the bar for craft as we hire more designers.

We are building the first Neuron performance engineering team in Tel Aviv. As a Machine Learning Performance Engineer, you'll help shape the direction of this team from the ground up - profiling and optimizing workloads across the full ML software stack, writing high-performance kernels, and improving the Neuron SDK that external developers depend on. You'll work at the boundary between software and hardware, collaborating directly with compiler, runtime, and chip design engineers to close performance gaps customers care about.

The team is new and small, which means broad scope, direct ownership, and real influence over the technical direction we take. If you enjoy digging into performance bottlenecks and turning analysis into measurable wins, this role is for you.

Key job responsibilities
Design and implement high-performance compute kernels for ML operations, leveraging the Neuron architecture and programming models.
Profile ML workloads end-to-end to identify bottlenecks - memory, compute, or communication - and drive optimizations through to a measured improvement.
Enhance the programming model and tooling that kernel and model developers rely on, improving usability and debugging workflows.
Identify and drive optimization opportunities across the Neuron software stack (compiler, runtime, frameworks).
Document software designs, operational runbooks, and performance findings so the broader team can build on your work.

A day in the life
You might start your morning reviewing profiling data from a customer's large diffusion model training job, tracing a utilization gap back to a specific kernel. After a design discussion with compiler engineers about a new operator fusion strategy, you spend the afternoon writing and benchmarking a kernel prototype. Later, you review a teammate's pull request for a runtime optimization and share your findings in a short write-up for the broader Neuron organization. Your work directly translates into faster model execution and lower cost for AWS customers running ML workloads at scale.

Required Acoustics Research Engineer
Why this role exists
Noise - not range, not cost - is what keeps aircraft out of cities. We exist because we believe an acoustic signature roughly four times lower than conventional eVTOLs changes what urban aviation is allowed to be, and this role sits at the exact center of that bet. You will own the methods that tell us, with numbers we can defend, how quiet the vehicle actually is - and where the next decibel comes from.
What you'll own:
Develop and validate source-noise prediction models for our novel lift and propulsion architecture, from first-principles aeroacoustics through empirically anchored corrections.
Design and execute acoustic measurement campaigns in anechoic facilities and at outdoor ranges, owning microphone array design, calibration chains, and data acquisition end to end.
Reduce rig and flight-test acoustic data into validated spectra, directivity maps, and component-level source rankings that directly drive vehicle design decisions.
Quantify community-noise exposure using EPNL, SEL, dBA, and tonality-corrected metrics, and translate the results into hard, testable requirements for design teams.
Build psychoacoustic models - loudness, sharpness, tonality, fluctuation strength - and run jury listening studies that connect physical metrics to human annoyance.
Run aeroacoustic simulations coupling CFD to acoustic analogy solvers (FW-H or equivalent), and reconcile predictions against measured data every design cycle.
Define and defend the vehicle noise budget at design reviews, allocating margins across subsystems and flagging acoustic risk before hardware is built.
Author measurement procedures, test reports, and model validation documents rigorous enough to anchor future certification arguments.