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As a Senior Software Engineer in the Platform Team, you will help deliver a smooth and consistent developer experience to our product/AI teams. This enables them to iterate quickly and deliver products faster to the market. The team aims to streamline development processes, reduce friction and provide developers with the right tools and infrastructure to build high-quality software efficiently which effectively aligns technical execution with our business objectives. Our techstack includes AWS, Python, RDS, EKS, ECS, RMQ and Redis.

You will:

You will have a direct impact on devs' daily smile :) , by focusing on their Devex and velocity through implementing and providing easy and intuitive self serve tools

Lead the design and implementation of infrastructure of complex, time-critical microservices systems, collaborating with AI, data, and product teams to ensure high-quality code using latest technologies while maintaining high developer velocity.

Drive improvements to backend infrastructure, focusing on scalability, reliability, and performance to enhance the daily experience and productivity of developers, including implementing intuitive self-serve tools.

Contribute to defining and realizing the technology vision of the company, evaluating and integrating new technologies to enhance platform capabilities including Gen AI, and troubleshooting complex issues to ensure platform performance and availability. Proactively establish and maintain CI/CD practices and processes including hybrid envs.

Enhance system scalability, performance, and reliability.

As a Senior Software Engineer, you will be responsible for designing and building core backend services and product capabilities, working on scalable systems that handle large volumes of security-related data, and developing robust features that improve security posture in cloud environments.

The ideal candidate will have strong software engineering foundations, a product-minded approach, and experience building distributed systems in production.

You will collaborate closely with Product, Research, and Engineering teams to deliver secure, performant, and reliable solutions end-to-end.

You will work within the engineering department, contributing to architecture decisions, improving code quality, scalability, and reliability, while maintaining strong ownership over development and delivery in a fast-paced environment.

Responsibilities
Design, develop, and maintain high-scale backend services and APIs.
Build features that identify and prevent cloud misconfigurations and security risks.
Write clean, maintainable, and testable code with high engineering standards.
Participate in system design and architecture discussions (scalability, reliability, performance).
Collaborate with Product, Research, and other engineering teams to define and implement end-to-end solutions.
Optimize performance and improve system reliability, monitoring, and observability.
Take ownership over services in production, including troubleshooting and incident resolution.
Contribute to engineering best practices: code reviews, technical documentation, CI/CD improvements.
Support and mentor other engineers, helping grow the teams technical excellence.

We're looking for an Infrastructure Engineer to go deep into that layer: build and own core proxy and networking infrastructure, not configure someone else's.

You bring the same range as our core infrastructure engineers - proxies and networking, browser internals, and cloud-native systems (Kubernetes, Docker, AWS) - with the depth to design and debug at every layer of the stack, plus a genuine specialization in networking and proxy systems.

What Youll be Doing:
Build core proxy infrastructure - reverse proxies, IP rotation, session management, intelligent routing.
Own IP pool health - reputation scoring, monitoring, blacklist recovery - across residential, mobile, datacenter, and ISP proxies.
Implement geo-targeting at country, city, and ASN level.
Work at the protocol level (TCP/UDP, sockets, HTTP/HTTPS/SOCKS5, HTTP/2/3) and the Linux networking stack underneath.
Build monitoring, health checks, failover, and bandwidth/usage metering for the proxy fleet.
Deploy and scale on cloud-native infrastructure (Kubernetes, Docker, Terraform).
Use AI-assisted development tools daily.

We need a Director to set the strategy for everything underneath it - cloud, security, and the delivery lifecycle - and to bring AI into how that infrastructure is run.

This is a director-level leadership role, not an IC stretch assignment. You will lead our DevOps organization - which already has a team leader running day-to-day delivery - and operate one level up: setting direction, owning the roadmap, making architecture and build-vs-buy calls with authority, and representing infrastructure at the leadership table. Close enough to the work to guide it in depth, senior enough to own it end to end.

What Youll be Doing:
Own cloud infrastructure and security as one strategy: architecture, scale, and the path to our next order of magnitude of growth, with our security and compliance posture (IAM, secrets, hardened baselines, SOC 2 / ISO 27001 audit readiness) built in from the start, not bolted on.
Own the SDLC end to end, and drive AI into every stage of it: measure delivery performance (lead time, deployment frequency, change failure rate) and use AI to improve each stage of the lifecycle - planning, code review, testing, release, and operations - alongside AIOps practices like automated/self-healing recovery and intelligent alerting.
Own FinOps as a strategic discipline: the cloud bill is a P&L line you're accountable for, with unit economics driven down every quarter.
Own vendor and contractual relationships across cloud, tooling, and infrastructure services - negotiating leverage, not just approving invoices.
Lead the DevOps organization through its team leader: set technical direction and standards, develop the team leader into a stronger manager, and grow the bench beneath them.
Partner as a peer with Backend, Infra, AI, Data, Product, Security and Finance leadership - infrastructure decisions get made with you in the room, not after.

We're building one of the most agentic, fast-moving AI products in the web search category - and we need a leader who can own it end to end. This is a cross-functional group leader role sitting at the intersection of AI product leadership and high-scale AI infrastructure: you'll lead the team building our agentic search product, and own the data infrastructure that indexes and caches the web at scale.

What Youll be Doing:
Set technical and product direction for an agentic web search AI system, from research and benchmarking through production and scale.
Bring deep, hands-on expertise in LLMs, agentic architectures, evaluation/benchmarking, and modern AI/LLMOps practices; make build-vs-buy and architecture calls with authority, not just strategy.
Lead and develop small group of two teams with top notch engineers, providing technical leadership, growing engineering managers, and driving execution across both product and infrastructure.
Drive the product to a category-winning position within an aggressive timeline.
Own the infrastructure supporting an API surface currently serving millions of calls/month, with a mandate to lead its growth to billions/month.
Bring proven, hands-on experience scaling high-throughput, high-availability infrastructure - not just AI-adjacent platform experience - including reliability, cost, and performance at that scale.
Own the data infrastructure needed to run the product, including large-scale data pipelines, storage, and operations.
Lead a worldwide web caching and indexing operation at high scale, ensuring freshness, coverage, and cost-efficiency of the index.

We are looking for a Senior Cloud Engineer to join our Infrastructure team in Tel Aviv. In this role, you will be a key contributor to our AWS CDK projects, taking end-to-end ownership of high-impact infrastructure shifts like our migration to EKS. This is a hands-on position where youll balance long-term architectural projects with day-to-day support for our R&D teams, ensuring our cloud environment remains scalable, secure, and developer-friendly.

Responsibilities:

As a Senior Cloud Engineer, you will be the backbone of our infrastructure, ensuring that our cloud environment is scalable, secure, and developer-friendly. You will:

Serve as a core contributor to our central AWS CDK (TypeScript) project, ensuring every cloud resource-from S3 buckets to complex networking rules-is defined as code.

Act as a consultant for R&D and platform teams, helping microservice owners deploy new services and spin up sandbox environments while maintaining security standards.

Take end-to-end ownership of high-impact infrastructure shifts, including playing a key role in the transition from ECS Fargate to EKS.

Manage the lifecycle and maintenance of critical infrastructure components, such as security/observability agents and hosted open-source solutions.

Collaborate with our Boston-based teams on shared global projects to ensure architectural alignment and cross-region infrastructure support.

Balance long-term project work with active participation in internal help channels, troubleshooting real-time issues and resolving conflicts in central tools.

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.

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.

We're looking for a Researcher who thrives on exploring the technical frontier and translating complex insights into real-world impact. This is a hands-on role, combining deep research with prototyping and cross-team collaboration, with the goal of driving innovation in Kubernetes internals, cloud infrastructure, and performance optimization.

Conduct deep research into Kubernetes internals, scheduling, autoscaling, and container resource usage.
Explore cutting-edge strategies for cloud resource optimization, cost efficiency, and performance engineering.
Design and deliver hands-on POCs, simulations, and benchmarks to validate new approaches.
Work closely with product and engineering teams to translate research findings into features and enhancements.
Stay at the forefront of the cloud-native and FinOps ecosystems, surfacing trends and opportunities.
Contribute to technical blogs, white papers, and internal knowledge sharing.

We're looking for a DevOps / Platform Engineer to own our infrastructure end to end- both sides of it.
Side one is our multi-tenant SaaS on AWS: you build it, you run it, you keep it fast, secure, and up. Side two is the part that makes this role unusual - our enterprise customers deploy Avon AI inside their cloud (and sometimes fully air-gapped, on-prem environments). You'll own that too: packaging the platform, running the installation with the customer's infrastructure and security teams, and being the person on the call when their environment doesn't look like anyone's reference architecture.
This is a customer-facing engineering role. You'll be in rooms with enterprise IT, security, and platform teams- walking them through our architecture, answering security review questions, and unblocking deployments. If you want a pure behind-the-scenes infra job, this isn't it. If you like owning infrastructure and being the trusted technical voice in front of a customer, read on.
You'll be our first dedicated DevOps hire, which means you inherit real systems in production and a lot of room to shape how they evolve.
Key Responsibilities
EKS, networking, data stores, CI/CD, cost, and uptime. Build and evolve it with Infrastructure as Code.
Maintain and improve our Helm charts and container distribution so Avon AI installs cleanly in a customer's Kubernetes cluster.
Plan and execute installations in customers' private clouds and on-prem/air-gapped environments, working hands-on with their infrastructure teams from kickoff through go-live.
Support security reviews and architecture discussions, and translate enterprise IT constraints back into our roadmap.
IAM, network segmentation, secrets management, WAF, and the controls enterprise customers audit us on.
Metrics, logs, tracing, and alerting for our SaaS, and self-contained monitoring for customers who won't ship telemetry outside their network.
CI/CD pipelines, ephemeral PR sandboxes, release automation, and local development so engineers ship quickly and safely.
For our SaaS tenants and for deployed customer installations, including on-call ownership of what you build.

We are seeking a highly experienced, AI-obsessed AI Senior Backend Engineer to join our core Engineering department. In this newly created, cross-functional role, you will be the driving force behind the companys internal AI transformation. You will act as the responsible for artificial intelligence, architecting, building, and deploying custom AI and automation systems from scratch to empower various departments and streamline operations.
Key Responsibilities
Architect, develop, and deploy tailored AI systems and applications from the ground up to solve organizational challenges.
Identify process bottlenecks across different teams and design robust backend automation solutions to optimize daily workflows.
Lead the hands-on integration of modern Generative AI models, APIs, and frameworks into the company's internal infrastructure.
Build scalable, high-performance backend microservices and APIs to support heavy data processing and AI workloads.
Act as the technical AI focal point, partnering closely with internal stakeholders and department heads to gather requirements and deliver high-impact technological solutions.
Collaborate with product, DevOps, and other teams to ensure that AI solutions are aligned with user needs and business goals.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across our platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose our capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how we evolve from AI-enhanced internal tools to customer-facing AI products.