דרושים » הנדסה » Senior backend Engineer (Applied AI)

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.

We are at the forefront of the AI revolution, delivering brand new accelerated compute platforms for global impact. Our Network Architecture group is seeking a talented and motivated Sr. Software Engineer to build the agentic workflows that our architects use in their daily work. The software at the center of this role is our hardware network simulation environment - you will design multi-step agent workflows over it, engineer the context that grounds them in our own specifications and source code, and optimize their runtime performance. If you are passionate about building the practical infrastructure that brings intelligent agents to life, we want to hear from you.


What you'll be doing:
Build agentic workflows - loops, graphs, and multi-step pipelines - that carry real hardware network simulation and analysis work end to end.
Engineer the context these workflows run on, turning our simulation models, specifications, design documents, and source code into context that makes agents accurate in our domain.
Work closely with network architects to understand their workflows and translate them into agent workflows they use daily.
Optimize the runtime performance of our simulation tooling on these platforms, including execution time, compute cost, and end-to-end latency.
Define evaluation and regression testing for agent workflows, so that changes to a prompt, a graph, or a context source are measurable.
Build observability across agent runs: what the agent did, where it failed, and why.
Champion guidelines for secure and reliable agent workflows, including data handling, access control, and interaction boundaries.
Serve as a key technical resource for solving sophisticated integration issues between agents and internal tooling.

We are looking for an experienced and hands-on Backend Engineer to be a key player in building high-scale Data Platforms and Products for our business teams.
This role involves working with large datasets and scalable systems, and developing internal tools to enable data-driven decision-making across the company.
The team builds the backend systems and data infrastructure the rest of the company relies on: internal products, APIs, event pipelines, and integrations with external tools. The data comes from the full product, at the volume of over 1.7 million daily active users. The features are multi-system: an API, a pipeline, and often LLM-backed flows that have to stay correct in production.
LLMs and agents are part of the job. We use them to write, review, and debug code, and we ship them inside the product: agents and LLM features that other teams depend on.
Key Responsibilities:
Develop internal tools and products for teams across Connecteam.
Build and maintain services and APIs that support complex, multi-step workflows.
Operate in a real-time, event-driven environment at high volume.
Create and manage data pipelines, including the checks that keep data trustworthy.
Design and ship LLM and agent features: tool use, prompts, evaluation, and the limits around cost, latency, and quality.
Use coding agents as part of normal delivery, and raise the quality of what they produce.
Take ownership of multiple systems and products.
Work in a multi-cloud environment (Azure/GCP/AWS).
Integrate third-party tools with our platform.
Translate ambiguous business requirements into a technical plan and lead it through to production.

We're looking for an experienced Software Engineer to join our Data Platform Group. In this key role, you will build the company data platform: cloud-based microservices and data pipelines that process on the order of 1M records/sec at low latency. Because the platform is the foundation other groups build on, your work has a direct impact on our customers and enables engineering, product, and research teams across the organization.
The Lakehouse team owns the data itself - how it is stored, organized, retained, and served. We own our analytical storage layer, the batch processing built on top of it, and the APIs through which customers and the rest of the company consume data. If you enjoy the problems that only appear at petabyte scale - physical data layout, query performance, storage cost, and retention - this is the role.
Responsibilities:
End-to-end ownership of our large-scale analytical storage layer: data modeling, schema and table design, partitioning, retention, and query performance.
Design and develop the batch processing layer over our data lake using Spark on EMR (Java and PySpark).
Build and evolve Java/Spring Boot services that expose our data through well-defined APIs to customers and to consumers across the company.
Own performance and cost: query optimization, file layout and compaction, cluster sizing, and storage efficiency at scale.
Research new technologies in the lakehouse and analytical-storage space and adapt them for use in our product.
Work closely with product, DevOps, and security teams.

Zipher is building the Autonomous Execution Layer for cloud data and AI workloads. Backed by $50M in funding , we dynamically orchestrate clusters, predict bottlenecks, and auto-heal infrastructure in real time — with zero human intervention . Our platform runs in production at global enterprise customers, including Fortune 500 companies , delivering mission-critical resilience and sub-second optimization Autonomous execution is a new category, and we are the ones defining it — already running in production inside Fortune 500 environments , where being wrong is not an option. The team is deliberately talent-dense ; every hire changes what we are able to build next. We are looking for a Senior Full Stack Engineer with 7+ years of development experience at leading startups to own Zipher’s entire product surface — the control plane that turns autonomous decisions into something engineering leaders can see, trust, and act on. You will own it end to end: the services and APIs behind it, and the real-time interfaces built on top of them. This is a backend-heavy role: about 70% backend and 30% frontend. It is best suited for candidates whose primary expertise and passion lie in backend development.
What You’ll Do
* Own the product surface end to end : architecture, services, APIs, and the real-time interfaces built on top of them
* Set the technical direction of the control plane — stack, data contracts, and the patterns every engineer after you builds on
* Build high-throughput services that turn live telemetry from production data and AI workloads into decisions, and the explainability layer that makes those decisions readable
* Work directly with the founders on what gets built next, and lead the technical moves that follow — from what enterprise customers need to the architecture that answers it
* Drive quality end to end: testing, CI/CD, observability, and the operational bar for a product Fortune 500 engineering teams run in production
What We Offer A product that is already winning : a new category of autonomous data platform, in production inside Fortune 500 environments A domain of your own : you lead it end to end — the architecture, the stack, and the patterns every engineer after you builds on A seat on the core team : you are one of the engineers deciding what the platform becomes, not one implementing someone else’s decisions Engineering problems that are genuinely hard : real-time telemetry at enterprise scale, distributed decisions, and an explainability layer teams trust in production Engineers worth learning from : a deliberately talent-dense team, direct work with the founders, and a bar that pulls everyone up Room to grow and to lead : your ownership widens as the platform does, and the people who lead moves here keep leading them Top-of-market compensation and a meaningful equity package Ready to own the control plane for an autonomous data platform? Hit Apply.

Were looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
Youll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. Youll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
Develop and improve our core algorithms.
Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices-not just standalone prototypes.
Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.

Required Senior Backend Engineer - Founding Team
About the role
Own the real-time backend of an AI sales coaching platform that's live, growing 2 week-over-week, and one month into go-to-market. You'll be the second backend engineer in a team of five, working directly with the head of engineering and CEO - building the systems that decide whether the product works: live audio pipelines, LLM orchestration under millisecond budgets, and the engine that turns real sales calls into personalized AI training.
We just found go-to-market fit. Now the backend has to keep up with it. That's where you come in.
A word of honesty:
This is an early-stage role at a small startup in its hardest, most exciting phase, and we'd rather you know exactly what that means before you apply.
You will own large parts of the backend, and ownership here is literal: you'll design it, build it, ship it, and get woken up by it. When a customer's live call drops audio, you'll trace it through the WebSocket, the event loop, and the LLM pipeline yourself. Some of our hardest problems aren't exotic - they're the unglamorous ones every fast-moving team accumulates: a background job that fails without telling anyone, a coaching signal that lands a beat too late, a regression that a replay test should have caught. We know exactly where it hurts, and we're hiring the person who ends it - not the person who files a ticket about it.
If you spent the last few years at a company like Wix or Monday, you already know what excellent engineering looks like at scale - strong typing, clean async code, real observability. What you may not have had recently is the feeling that your work visibly moves the company every single week. Here it does. The trade is real: less process and certainty, far more impact and speed. If that trade excites you rather than worries you, keep reading.

We are looking for an experienced and hands-on Backend Engineer to be a key player in building high-scale Data Platforms and Products for our business teams.
This role involves working with large datasets and scalable systems, and developing internal tools to enable data-driven decision-making across the company.
The team builds the backend systems and data infrastructure the rest of the company relies on: internal products, APIs, event pipelines, and integrations with external tools. The data comes from the full product, at the volume of over 1.7 million daily active users. The features are multi-system: an API, a pipeline, and often LLM-backed flows that have to stay correct in production.
LLMs and agents are part of the job. We use them to write, review, and debug code, and we ship them inside the product: agents and LLM features that other teams depend on.
Key Responsibilities:
Develop internal tools and products for teams across Connecteam.
Build and maintain services and APIs that support complex, multi-step workflows.
Operate in a real-time, event-driven environment at high volume.
Create and manage data pipelines, including the checks that keep data trustworthy.
Design and ship LLM and agent features: tool use, prompts, evaluation, and the limits around cost, latency, and quality.
Use coding agents as part of normal delivery, and raise the quality of what they produce.
Take ownership of multiple systems and products.
Work in a multi-cloud environment (Azure/GCP/AWS).
Integrate third-party tools with our platform.
Translate ambiguous business requirements into a technical plan and lead it through to production.

Appdome's mission is to protect every mobile app in the world and the people who use them. We are the leader in AI-native mobile business protection, providing cyber and fraud teams with an agentic platform that builds, monitors, and maintains security defenses in Android and iOS apps — with no SDKs, no coding, and no disruption to engineering cycles.
Our platform delivers over 400 security, anti-fraud, anti-bot, and API protection capabilities, powered by deep learning models trained on a decade of mobile defense data and trillions of live threat events. From build time to runtime, Appdome's AI Agents help mobile brands detect, investigate, and respond to threats faster than ever — recognized as the best AI Platform for Cyber Resilience at RSA Conference 2026 for the second consecutive year. Leading financial, healthcare, m-commerce, and B2B brands rely on Appdome to secure over 50,000 mobile apps and protect more than 1 billion end users globally.
Appdome is an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics.
About the Role We are looking for a talented Software Engineer to join Appdome's Identity Group. You will take part in building advanced fraud detection capabilities, working closely with security researchers and data teams to develop cutting-edge solutions that protect hundreds of millions of mobile devices.
Responsibilities Design and develop core components of fraud detection systems from early prototyping through production-ready releases. Build scalable backend services and distributed systems that process data at scale. Work with large-scale datasets and collaborate with data teams to create actionable signals and features. Write high-performance, production-quality code in C and C++ optimized for reliability and performance. Take ideas from rapid proof-of-concept through to production, tackling complex engineering challenges across the full development lifecycle.

we are using technology to transform transportation around the world. From changing a single persons daily commute to reducing humanitys collective environmental footprint - weve got huge goals.
As a Senior Backend Engineer in the Routing team, you will be responsible for designing, building, and scaling the backend systems that power our companys core products. Youll work on high-impact, distributed services that manage geospatial data, large-scale databases, real-time events, and mission-critical workflows used across multiple teams and regions. Your work will directly influence system reliability, performance, and developer productivity at scale.
You will collaborate closely with Product, Infrastructure, Data, and other engineering teams to define system boundaries, evolve shared services, and ensure our backend platform can support rapid product growth. You will be part of a highly skilled and motivated engineering group responsible for building robust, scalable, and maintainable backend systems that operate in real time and at high load.
What Youll Do:
Design, build, and scale backend systems and data pipelines that power our companys GIS and mapping platform, from initial design through production deployment and long-term ownership.
Own features end-to-end with strong accountability, including design, implementation, testing, deployment, monitoring, and ongoing maintenance.
Develop and maintain Python-based data pipelines that ingest, process, and transform large-scale OpenStreetMap (OSM) and other geospatial datasets.
Model, store, and query complex geospatial data using PostgreSQL and PostGIS, including road topology, zones, polygons, points, and other business-critical spatial entities.
Optimize database schemas, spatial indexes, and queries to efficiently handle large datasets and high-throughput workloads.
Build and maintain backend services and APIs that expose geospatial and mapping data to other backend services, frontend applications, and data consumers.
Collaborate closely with other backend teams, frontend teams, data teams, and infrastructure to align on data contracts, system boundaries, and cross-team initiatives.
Improve system reliability, observability, and performance through monitoring, alerting, benchmarking, and continuous optimization.
Contribute to architectural decisions, technical standards, and best practices, while mentoring other engineers and raising the overall engineering bar.