דרושים » תוכנה » backend engineer - ml platform

we're looking for a senior backend engineer to help build and scale the Machine Learning platform that powers how the company uses 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 at the company: 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.

anyone can ship an ai agent. shipping one thats right ~98% of the time, with no human checking its work, takes different engineering. thats what this team builds.
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 at the company: 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
what you'll need
6+ years building production-grade backend systems that real users depend on
hands-on experience shipping llm-powered features to production, including honest reckoning with non-determinism, hallucination, and cost
an evaluation mindset - you don't call a change "better" without a measurement, and you're comfortable with the statistics that back that claim
a track record of debugging distributed systems in production, and the discipline to find root cause across service boundaries
fluency with agentic coding tools like claude code or cursor, including the habit of treating ai-generated code with a senior reviewer's skepticism
the ability to pull your own numbers, design your own experiments, and measure your own impact
ready to work in an office environment most days of the week

were looking for a senior-level backend engineer to join our growing engineering team in tlv.
this role will require you to bring features to the finish line while collaborating closely with other developers, as well as with the product, QA, data, and Machine Learning teams-all with the goal of delivering a seamless, joyful experience for our customers.
we believe three things matter for every role at the company: 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 youll:
help build our products using microservices written in typescript (using nestjs).
leverage our in-house autonomous tools to manage all development flow, from setting up our development environment to creating git branches and deploying services to production
collaborate closely with the product team, in a self-sufficient squad to achieve the squads okrs
be the owner of your domain to ensure code quality, performance, and scalability of our products
coach and mentor other developers and help them elevate their skills.

A Senior AI/ML Engineer role on our Data team, sitting at the intersection of AI engineering, data science, and data platform. You'll build agentic, LLM-based systems that turn vast amounts of unstructured information into high-quality structured signals - powering our products and surfacing legal risk - owning each solution across the full lifecycle, from exploration to production. You'll work closely with data engineers, data scientists, product managers, and legal experts to turn ambiguous problems into scalable AI capabilities. This is a hands-on role for someone with strong engineering judgment, deep data/ML expertise, and a focus on reliability beyond the prototype stage.

What You'll Do

Design, build, deploy, and operate production AI systems: agentic, LLM-based extraction workflows and batch/online inference pipelines and owning them from exploration through deployment, monitoring, and iteration.
Define quality metrics and build evaluation datasets, testing processes, and feedback loops for ML and LLM systems; monitor accuracy, latency, cost, and data quality, and investigate and fix failures.
Explore large, complex datasets to surface useful features and patterns, and translate findings into production-grade implementations.
Partner with data engineers on ingestion, transformation, orchestration, and storage to deliver dependable data products.
Use AI-assisted coding tools (e.g., Claude) effectively while maintaining strong technical judgment and ownership of your systems.
Help shape engineering standards for production AI: testing, observability, reproducibility, data lineage, versioning, and safe deployment.

We are looking for an experienced Applied AI/ML Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform, designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers. You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops, enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productize agentic solutions - including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing

Required Founding ML 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:
Youll build the platform our data science team works on.
That means the pipelines, tooling, and infrastructure that let data scientists train, deploy, and monitor models without needing an engineer at every step - and working alongside them to get their models onto the platform when they do.
The work is predictive ML: churn, purchase timing, and recommendations, running for enterprise clients at scale.
Youll help shape how ML is built and operated here as the team grows.
Responsibilities:
Build the ML Platform - Design the pipelines, tooling, and infrastructure the data science team builds on.
Enable Self-Service - Make it possible for data scientists to train, deploy, and monitor models without an engineer in the loop.
Implement Models on the Platform - Work alongside data scientists to take their models from notebook to production.
Own Features at Scale - Build the feature pipelines that keep training and inference consistent.
Keep Models Healthy - Build the monitoring and retraining that keep predictions accurate over time.
Shape How We Build ML - Help define the tooling, standards, and practices for ML as the team grows.

we are seeking a software engineer with a passion, knowledge, and experience for taming, training, and controlling LLM and ML models, for diverse use cases and users. In this role, you will take part in all the steps of shipping GenAI, from idea to enterprise-grade solution, across all stages of the product development lifecycle.

Responsibilities:
Get deeply acquainted with a problem domain, investigate and prototype technologies and architectures for an adequate solution
Collaborate with product and engineering teams to translate customer-facing features into detailed product specifications and solution design
Build GenAI-powered applications/agents from the ground up - from data collection and model training to deployment and production monitoring
Conduct experiments and model evaluations, analyze performance metrics, and iterate rapidly to maximize accuracy and effectiveness
Develop and propagate best practices, including reusable tools or frameworks, to ensure robust and maintainable GenAI engineering
Stay updated with emerging trends and advancements in AI - including new models, frameworks, and techniques - and bring forward innovative ideas for their adoption

As a Senior Software Engineer on the Cortex Platform Application team, you will be a hands-on technical leader responsible for designing, building, and operating critical backend platform services and customer facing features used across Cortex. This role is for an engineer who can combine deep platform/backend expertise with strong ownership, practical execution, and cross-team influence. You will help build scalable and reliable services, guide technical decisions, improve engineering methods, mentor engineers, and raise the quality of how the team designs, ships, documents, tests, and operates its systems. The team owns a broad set of platform capabilities, including areas such as application configuration, dashboards and reporting infrastructure, metrics, multi-tenant and MSSP infrastructure, log forwarding, notifications, auditing, access and permission engines, filtering and querying infrastructure, and other shared application platform domains. We develop customer facing features, as well as internal capabilities serving other development teams within cortex. We are looking for someone who is not only strong technically, but also enjoys being a part of a high-performing team, taking ownership from within, and helping turn strong execution into repeatable engineering practice. A successful candidate will quickly become a trusted part of the team by shipping meaningful work, learning the team's domains, and helping others move faster. Over time, this person will help the team increase delivery capacity while also improving how work is designed, documented, tested, operated, and shared across the organization. The right person will not act as an external advisor or abstract architect. They will join the team's day-to-day work, earn trust through execution, and help create organized technical influence from inside the team.
Design, build, and operate scalable, reliable, and secure backend services for the Cortex Platform.
Act as a hands-on technical leader, writing and reviewing production code for critical platform services.
Lead technical design for complex backend and platform domains, balancing long-term architecture with practical delivery.
Own and improve core platform capabilities used by internal engineering teams and Cortex customers.
Drive operational excellence, including observability, monitoring, alerting, incident response, reliability, and participation in on-call.
Improve engineering methods around documentation, testing, design reviews, ownership models, and production readiness.
Mentor senior, mid-level, and junior engineers through design guidance, code reviews, production work, and day-to-day collaboration.
Collaborate closely with product managers, architects, security experts, QA, DevOps, and other engineering teams.
Help internal customers use platform capabilities correctly by creating clarity, alignment, and practical technical guidance.
Identify gaps in systems, processes, and ownership, and lead improvements without adding unnecessary process or friction.
Contribute to a calm, practical, low-ego engineering culture focused on ownership, execution, and continuous improvement.

We are looking for an experienced Backend Engineer to help build the core infrastructure behind our company's agentic AI system.
our company builds AI Digital Employees who help cyber teams close the execution gap. Our first AI Digital Employee, Alex, learns, understands and takes away the burden of Identity and Access Management (IAM) tasks - proactively completing the organization's cyber objectives.
About the Role:
We are looking for a strong Backend Engineer with 5+ years of experience to help build the core infrastructure behind our company's agentic AI system. This is not another workflow tool: we are building something new, in a category that does not really exist yet. You will work on the services, orchestration, integrations, data models, retrieval layers, guardrails, and evaluation infrastructure that let AI agents reason over enterprise context and take reliable action.
This role is deeply product-facing and infrastructure-heavy. Our customers are large enterprises that manage tens of thousands of identities, so the systems we build need to be secure, observable, reliable, and able to operate at real scale.
What You'll Do:
Build production backend services in Python that power our company's agentic AI platform.
Design and implement AI-agent orchestration, tool-calling infrastructure, workflow execution, guardrails, and evaluation loops.
Integrate with enterprise applications, identity providers, SaaS tools, and customer data sources so agents can understand context and execute tasks safely.
Build systems that ingest, model, and reason over organizational processes, permissions, policies, and internal knowledge.
Solve scale and reliability challenges for large enterprise environments with tens of thousands of identities.
Collaborate closely with product, AI, and customer-facing teams to turn ambiguous business processes into reliable automated experiences.
Own reliability, observability, performance, and security for production systems.