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What Youll Do:
Architect, design, and build high-performance, scalable backend systems.
Work with advanced algorithms, data structures & distributed computing.
Design and implement robust, secure APIs.
Use best practices to ensure flexibility, extensibility, and maintainability.
Develop SQL & NoSQL persistence solutions with optimal data modeling & performance.
Build and scale cloud-based infrastructures.

This is a hybrid position.

We seek a skilled Senior Infra Backend Engineer to join our R&D office in Tel Aviv, working in a hybrid and flexible model.
Youll collaborate with a top-tier R&D team and take your professional growth to the next level. Our backend infra teams work mainly with Python and Go, but we welcome talented backend engineers from any language background.

Key Responsibilities

Design and build scalable pipelines processing billions of events daily.

Dive deep into infra technologies and optimize them at scale.

Lead major efforts from idea initiation - design - implementation.

Tackle complex challenges in scalable architecture within our AWS environment.

Integrate and leverage top industry technologies such as Kafka, Iceberg, DynamoDB, Postgres, EMR, Kubernetes, Druid, Snowflake, and more.

We are seeking an experienced and highly driven Senior Hardware NPI Program Manager to lead the successful introduction of next-generation hardware products from concept through volume production. In this role, you will partner cross-functionally with Hardware Engineering, Operations, Product Management, and Quality teams to ensure flawless execution of product launches. The ideal candidate has a proven track record of managing complex hardware programs, driving schedules and deliverables across global teams, and enabling a seamless transition from development to scalable manufacturing. There will also be opportunities to manage internal HW,SW and Operational projects with partners.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless company ronauts who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
Key Responsibilities
Own and drive the end-to-end NPI program lifecycle for hardware products, from concept through ramp to sustaining to EOL
Develop, manage, and track detailed project plans, schedules, risks, and dependencies across multiple functions and suppliers
Partner with Engineering to translate product requirements into manufacturable designs and processes
Coordinate with Supply Chain, Manufacturing, and Quality to ensure suppliers and factories are ready for builds and scale
Lead cross-functional meetings, program reviews, and executive updates to communicate status, risks, and recovery actions
Proactively identify program risks, define mitigation strategies, and drive accountability for execution
Ensure manufacturing readiness by leading build events (EVT, DVT, PVT, MP) and ensuring exit criteria are met at each stage
Align stakeholders across engineering, operations, and customer support for smooth product release into the field
Continuously improve NPI processes and best practices to increase efficiency and scalability.

we are looking for a passionate and detail-oriented Product Owner & Engineer to join our team. In this role, you will collaborate closely with development teams to define new features, contribute to functional requirement documentation (FRD), and ensure seamless UX design and implementation.
A core part of this role is maintaining close alignment with customers. You will actively engage with customers, gather feedback, understand real-world use cases, and translate their needs into clear product requirements. You will work closely with Support and QA to ensure our testing strategy and supportability reflect actual customer environments and workflows.
Beyond core development, you will assist in critical escalations, manage complex or high-visibility installations, and develop tools that enhance the overall user experience. You will leverage telemetry data, production insights, and direct customer feedback to continuously refine and improve our products, ensuring they deliver measurable value in real-world deployments.
Key Responsibilities
Collaborate closely with Product and Engineering teams to define, refine, and prioritize features that directly address real customer needs and business impact.
Translate customer requirements and field insights into clear, structured Functional Requirement Documents (FRDs), while actively contributing to UX discussions to ensure intuitive and seamless user experiences.
Work closely with QA and Support to align testing strategies and troubleshooting workflows with real-world customer environments, ensuring reliability, operability, and supportability at scale.
Serve as a technical and product focal point during critical customer escalations and high-visibility deployments, ensuring timely resolution and long-term improvements
Develop tools and scripts to enhance user experience and operational efficiency.
Leverage telemetry, usage analytics, and direct customer feedback to drive data-informed decisions and continuously improve product performance and adoption.
Proactively identify risks, gaps, and cross-team dependencies, removing roadblocks to ensure successful delivery and measurable customer outcomes.

LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new, and they ship into production rather than a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.

03What you'll do
Invent and ship optimizations. Design new strategies across every layer of a request: prompt and context compression, tool pruning, file and dev-command trimming, semantic caching, cache-prefix stabilization. Then take them all the way to the hot path. Each one has to cut tokens on real traffic without degrading output.
Prove every win. Build the evals and the token/quality instrumentation that separate a genuine saving from a plausible-sounding one. An optimization only ships when the numbers back it.
Live in the caching trade-offs. Provider prompt caches only pay off on byte-stable prefixes, and a naive rewrite can cost more than it saves. A lot of the job is knowing exactly when an optimization actually wins: deterministic transforms, per-session decision pins, semantic dedup.
Build and own the gateway that runs them. The real-time proxy that carries production LLM traffic, with streaming, retries and backpressure, executing every optimization on the live request path.
Keep it fast and deploy anywhere. Defend a tight latency budget on the hot path, and package the whole thing so it installs cleanly into any customer's cloud, whether that's AWS, GCP, Azure, containers or Kubernetes, and upgrades without drama.
Own systems end to end. From first commit to code serving live customer traffic, plus the spend-visibility and governance product built on top, with a direct line to the founders.

Connect is a Node CLI and a signed tray app for macOS, Windows and Linux. It points a developer's AI coding tools at our gateway by writing each vendor's own config, signs them in, keeps their credentials fresh, and repairs itself when something drifts. For IT, it ships the artifacts they need to roll it out across a fleet.

It's real, it's shipping, and customers are using it. You'd own it.

03What you'll do
Own Connect. The CLI, the tray app, updates, docs, and what ships next.
Write into other apps' config without breaking them. Back up what you touch, roll back cleanly, and never leave someone's setup worse than you found it.
Keep up with vendors. Config shapes change under us. Check before writing, and fall back to a manual path instead of guessing.
Stay clear of antivirus. Persistence and credential helpers are exactly what EDR watches for. Measure what actually trips a scanner instead of assuming.
Ship for managed fleets. Profiles, policy, credential helpers, and silent rollout. That's what an admin needs to push this to a few hundred laptops at once.
Make it debuggable from far away. Health checks, repair commands, and diagnostics that carry metadata only. Never prompts, never keys.

Every LLM call carries tokens that don't need to be there, and the provider bills for all of them. Anyray sits between a company's users and the models and takes that waste out as the request goes through. The result is usually up to 60% off the inference bill with no change in output, and every saved token accounted for. It runs inside the customer's own cloud, so nothing leaves their network, works with whatever provider and model they already use, and goes in within minutes.

02What you'll do
Build your own pipeline. That means targeted outbound to engineering and platform leaders, starting in the Israeli ecosystem and moving into the US.
Run deals start to finish: demo the product, handle the technical conversation yourself, and carry evaluations through to a signature.
Own the GTM engine: the outbound, the deal process, the playbooks, the collateral.

What You Will Be Doing
Own the co-build agenda with AWS & GCP
Drive our active co-build initiatives with AWS/GCP product and engineering teams from scoping through launch.
Own the technical relationship with AWS/GCP product managers and engineers on the EKS/GKE, Karpenter, and container services teams. Be the person they call, and the person who can answer without scheduling a follow-up.
Track the EKS/GKE and AI/ML roadmap closely enough to tell us what it means. Dynamic Resource Allocation, MIG and time-slicing landing in Karpenter and Auto Mode/Auto Pilot, managed control plane work, and pod isolation all move the ground under our product. You will be the first person to see it coming.
Continuously validate that we run on where AWS/GCP are going, not just where it is. As AWS/GCP abstracts the node further, our agent, our scheduler, and especially our GPU device plugin need to keep working. Confirming that, early, is a standing part of the job.
Bring AWS/GCP requirements and constraints back into R&D as real engineering input, and represent our architecture back to AWS/GCP accurately enough that they design around it.
Look for opportunities to be a design partner and later a launch partner.
Technical depth on us and EKS/GKE
Be the person who can explain how we work on EKS/GKE to any technical audience, without an SE in the room: real-time pod right-sizing, HPA and replica management, intelligent pod placement, Karpenter and node optimization, Spot, and GPU fractionalization and right-sizing for inference workloads.
Hold your own with AWS/GKE Solutions Architects and customer platform engineers on scheduling, autoscaling, resource management, and where our approach differs from static MIG and time-slicing.