We're looking for a Senior Backend Engineer to help build and scale the Machine Learning Platform that powers how we use AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
We believe three things matter for every role: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll:
Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units
Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management
Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring
Evaluate and choose the right tools and technologies based on workload demands and performance requirements
Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals
Identify and fix reliability, scalability, and performance gaps before they become problems.

We're looking for a Senior Software Engineer to join us at a moment when AI is reshaping how software gets built and how attackers operate. You'll ship production code in a core engineering team, own features end-to-end, and help push our engineering org to be genuinely AI-native, both in what we build and in how we build it.
This is a hands-on role for someone who wants real ownership, works well with product and research, and is excited to use AI as a daily force multiplier.
Responsibilities:
Design, build, and ship features across our platform, from architecture through production
Own components end-to-end: performance, reliability, security, and long-term maintainability
Partner with product managers, security researchers, and other engineers to turn hard problems into shippable products
Lead the integration of AI capabilities (LLMs, agents) into the product where they create real user value, not just where they look good in a demo
Drive the adoption of modern AI coding tools (Cursor, Claude Code, Copilot) as part of your daily workflow, and actively mentor the team to level up their AI-driven development
Raise the bar on code quality, review culture, and technical decisions
Mentor other engineers and contribute to how the team works, not just what it ships.

Required Senior Back-End Developer
Description
About Us
We are bootstrapped and has been profitable since day one. We built one of the most efficient programmatic acquisition engines on the planet: processing billions of ad decisions a day, generating terabytes of live behavioral data, and turning it into margins most SaaS companies would kill for.
At the core of that engine is a team of data scientists and ML engineers running tens of billions of sub-second predictions a day, deciding in milliseconds which impression is worth buying and what its worth.
Now were taking everything weve learned about how people move through the web and putting it into our most innovative consumer product yet: Ace AI, an AI-native browser already reaching millions of people, built to simplify and improve everyday online life with the help of AI.
This is a rare opportunity: a category that hasnt meaningfully changed in over a decade, and a profitable company with the data and the acquisition engine to win it on its own terms.
Come build it with us.
What Youll Do:
Own backend features end-to-end, from technical design to production monitoring.
Design, build, and scale backend services and APIs, primarily in Python.
Own core flows such as authentication, subscriptions, and the services behind our AI features.
Keep production fast and reliable, working closely with product, AI, and data teams.
Build AI-native: work with Cursor and Claude Code agents every day and help make our codebase agent-friendly.

We're looking for a Senior Backend Engineer to help push our AI customer-service agents closer to fully autonomous insurance service. You'd be joining the team behind the agent that talks to real customers every day over email, phone, chat, and SMS - handling policy questions, claims conversations, and verified documents end-to-end, with no human in the loop. This isn't a demo or a copilot. Real customers, real money, real stakes, every day.
We believe three things matter for every role: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll:
Design, build, and own production LLM agent systems end-to-end - deciding how the agent thinks, what it's allowed to do, and how it fails safely
Work on our real-time voice agent, where the hard problems are conversational: interruptions, mid-sentence tool calls, and keeping response times imperceptibly fast
Shape the next generation of our knowledge base - how the agent's knowledge is structured, curated, and served, so answers stay grounded as coverage grows
Ship changes as controlled experiments, measure them against thousands of replayed real conversations on our in-house eval platform, and graduate winners into production
Track cost per conversation across millions of LLM calls and find the wins that cut spend without cutting quality
Own what the model knows and says - the customer context it sees, the limits on what it may reveal, and the guardrails against confident nonsense
Build and operate scalable backend services in TypeScript/Node on Kubernetes/AWS with deep observability - if you ship it, you own it.

We're looking for a Senior Backend Engineer to help build and scale the Machine Learning Platform that powers how we use AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production.
We believe three things matter for every role: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll:
Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units
Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management
Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring
Evaluate and choose the right tools and technologies based on workload demands and performance requirements
Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals
Identify and fix reliability, scalability, and performance gaps before they become problems.

Our engineering team is at the core of how modern data security is built. We are redefining how organizations understand and protect their most sensitive data in a world of growing scale, speed, and complexity. By combining high-performance systems with intelligent, AI-driven capabilities, we challenge traditional approaches and push the boundaries of what data classification can achieve.
Were looking for engineers who thrive in open-ended environments, are energized by hard problems, and are motivated to build solutions that dont yet exist. This role is about more than writing code. Its about shaping how intelligent systems detect, interpret, and protect data across cloud and endpoint environments.
In this role, you will play a central part in designing and building a core classification engine that powers multiple security solutions across the platform. Your work will directly influence how different products identify risk, enforce protection, and operate at scale. Youll work at the intersection of large-scale systems and applied AI, turning raw signals into accurate, reliable insights that help customers secure their data with confidence.
Key Responsibilities
Build and scale AI-driven data classification and detection capabilities for next-generation data security products.
Design systems that identify, classify, and protect sensitive data in real time across cloud and endpoint environments.
Lead technical design discussions and influence architecture for large-scale data processing systems.
Partner with product, infrastructure, and other engineering teams to deliver tightly integrated solutions.
Solve complex engineering challenges related to scale, performance, reliability, and operations.
Raise the technical and collaborative bar by mentoring, sharing knowledge, and improving team practices.
Proactively suggest ideas, lead initiatives, and drive continuous improvement across the product and engineering processes.

We're backed by tier-1 global VCs, led by second-time founders, and moving fast because the problem demands it. There are no silos here. No "that's not my area." If it ships, you own it.
We're hiring a full-stack software engineer who wants end-to-end ownership - backend, frontend, infrastructure touchpoints, and AI-driven workflows.
If you're looking for narrow scope or strict boundaries, this is not the role. If you like building real systems and taking responsibility for outcomes, read on.
What you'll do:
Own features end-to-end - backend, frontend, APIs, and behavior in production
Build AI-native workflows:
Agent-driven actions
Human-in-the-loop interfaces
Explainability and control surfaces
Use Claude / Codex / AI copilots as default tools - not optional helpers
Work directly with product and customers to refine behavior
Debug production issues across the stack (no handoffs)
Ship fast, learn fast, fix fast
Take responsibility for what you build - including after it's live

Required Principal/ Senior Backend Engineer - Data Classification
Job Summary
Our engineering team is at the core of how modern data security is built. We are redefining how organizations understand and protect their most sensitive data in a world of growing scale, speed, and complexity. By combining high-performance systems with intelligent, AI-driven capabilities, we challenge traditional approaches and push the boundaries of what data classification can achieve.
Were looking for engineers who thrive in open-ended environments, are energized by hard problems, and are motivated to build solutions that dont yet exist. This role is about more than writing code. Its about shaping how intelligent systems detect, interpret, and protect data across cloud and endpoint environments.
In this role, you will play a central part in designing and building a core classification engine that powers multiple security solutions across the platform. Your work will directly influence how different products identify risk, enforce protection, and operate at scale. Youll work at the intersection of large-scale systems and applied AI, turning raw signals into accurate, reliable insights that help customers secure their data with confidence.
Key Responsibilities:
Build and scale AI-driven data classification and detection capabilities for next-generation data security products.
Design systems that identify, classify, and protect sensitive data in real time across cloud and endpoint environments.
Lead technical design discussions and influence architecture for large-scale data processing systems.
Partner with product, infrastructure, and other engineering teams to deliver tightly integrated solutions.
Solve complex engineering challenges related to scale, performance, reliability, and operations.
Raise the technical and collaborative bar by mentoring, sharing knowledge, and improving team practices.
Proactively suggest ideas, lead initiatives, and drive continuous improvement across the product and engineering processes.

Required Founding Engineer
Tel Aviv
Full-Time
About Us:
Were building the first AI-native autonomous revenue engine for D2C brands, a new category of AI infrastructure replacing the entire revenue stack.
If you want to build category-defining infrastructure with a team that ships weekly, lets talk.
About the Role:
This is a broad, hands-on engineering role.
Youll work directly with the founders and own the most challenging parts of the platform end to end.
The focus is backend and infrastructure, but youll work across the full stack whenever a problem calls for it.
Youll help shape the technical direction, and how engineering works as the team grows.
Responsibilities:
Own the Challenging Problems - Take the most complex parts of the platform end to end, from architecture through production.
Shape Architectural Decisions - Help define service boundaries, data models, and technical direction as the platform scales.
Build the Core - Design and ship the systems at the heart of the platform.
Scale the Data Pipeline - Build the ingestion, processing, and orchestration layers that move data through the platform reliably.
Own Infrastructure - Shape deployment, observability, and reliability across cloud infrastructure and Kubernetes.
Build End to End - Work across the full stack when the problem calls for it, including the surfaces your services power.

What am I going to do?
Build and evolve our production LLM pipelines, turning unstructured text into structured data that the product consumes.
Design and operate large scale batch and near real time data processing, including correctness, freshness and cost.
Build the retrieval and serving layers that make our data usable in real time.
Decide what runs on which LLM model, and prove the choice with numbers rather than intuition.
Keep cost and latency predictable as volume grows: throughput, failure handling and monitoring.
Build agentic workflows that makes the whole team faster, and help set the standards others work to.