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we are looking for a MLOps Engineer.
As an MLOps Engineer, you will work at the intersection of backend engineering and machine learning, building the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features used by hundreds of thousands of families.
What you'll be doing -
Build Production AI Systems - Design and implement backend services and end-to-end AI solutions that integrate Deep Learning models, Computer Vision algorithms, and GenAI into real features.
Power Experimentation and Validation - Contribute to the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Develop Data Pipelines - Build and maintain scalable data pipelines and big-data solutions that feed AI capabilities reliably and with an eye on cost and scale.
Own What You Ship - Take features end-to-end within your squad, from planning and design through implementation, deployment, and monitoring in production.
Cross-functional Collaboration - Work closely with the Algorithms and Data teams to tackle complex, real-world problems, helping translate research into shippable, maintainable systems.
Backend Guild Engagement - Actively contribute to a backend guild that drives Software Engineering and System Design best practices, guidelines, and standards across the R&D team.

we are looking for a MLOps Team Lead.
As an MLOps Team Leader , you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.
What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.
Who You Are:

we are looking for a Senior AI Agents & Full-Stack Engineer.
What You'll Do:
Build advanced AI agent systems for creative and marketing workflows
Design and implement scalable backend services in Python
Develop frontend experiences using React and TypeScript
Work closely with AI researchers, product leaders, and designers
Help shape the architecture and direction of a new product from its earliest stages

Required Full Stack Developer
Full-time
Company Description:
A dynamic technology firm dedicated to building exceptional products using modern technologies for the world's most admired companies. We pride ourselves on innovation, collaboration, and delivering excellence in every project.
Job Description:
Design and develop web applications from concept to deployment.
Collaborate with UX/UI designers and backend developers to create a cohesive product.
Manage databases and optimize server-side logic for performance.
Implement and maintain APIs for frontend-backend integration.
Troubleshoot and debug issues across the application stack.

We're looking for engineers who are curious, innovative, and passionate about solving difficult system-level problems while embracing AI as part of modern engineering.
What you'll do:
Design and execute end-to-end integration and validation scenarios across multiple product domains.
Validate new features and ensure seamless interoperability between distributed system components.
Investigate complex cross-component issues through deep technical analysis and root-cause investigation.
Analyze application and system logs to identify issues and drive resolution.
Develop a strong understanding of system architecture, data flows, and interactions between services.
Collaborate closely with Software Engineers, Architects, Product Managers, and cross-functional engineering teams.
Participate in feature planning by providing an integration and validation perspective early in the development lifecycle.
Continuously improve validation methodologies, engineering processes, and overall product quality.
Leverage AI tools to accelerate troubleshooting, technical research, documentation, and engineering productivity.
Challenge existing approaches and introduce innovative ideas that improve engineering efficiency and product quality

We are looking for a highly motivated and experienced Sr. Principal DevOps Engineer to design, build, and maintain scalable, secure, and highly available cloud infrastructure for our next-generation products.
The role drives DevOps activities across the engineering organization, with a strong focus on automation, CI/CD optimization, infrastructure as code, and the use of modern AI tools to improve development efficiency, reliability, and the software delivery lifecycle.
What you'll do:
Design, implement, and maintain scalable, secure, and highly available cloud infrastructure (e.g., AWS, Azure, GCP)
Develop and optimize CI/CD pipelines to improve deployment speed and quality
Monitor system performance, troubleshoot issues, and ensure high uptime and reliability
Automate provisioning, configuration management, and monitoring using tools like Terraform, Ansible, or similar
Collaborate with software engineers to understand and support their infrastructure needs
Define and enforce best practices for DevOps, infrastructure as code, security, and observability
Lead AI initiatives within the engineering group by leveraging AI-driven tools and integrating them into the CI/CD pipeline to enhance code quality and optimize the software development lifecycle

We are looking for a hands-on Senior Applied AI Evaluation Engineer to improve how our RAN R&D organization analyzes engineering tickets, supports Root-Cause Analysis, recommends ownership, and learns from resolved cases.
You will build rigorous evaluation datasets, establish meaningful baselines, compare internal and approved external tools, analyze failure modes, and prototype improvements across retrieval, classification, prompting, agent workflows, and model selection.
This role is focused on measurable, evidence-based improvement rather than AI demonstrations. You will assess whether AI-generated conclusions are accurate, grounded in evidence, appropriately calibrated, and useful to engineering teams.
As a secondary area of focus, you will analyze engineering workflows at case and team level to identify bottlenecks, handoffs, dependencies, and opportunities for process improvement.

we are looking for a hands-on technical leader to build and operate a secure local large-language-model platform for the company. The platform will allow engineering and business teams to use generative AI with proprietary source code, product documentation, technical standards, test artifacts, support knowledge, and other approved internal data while keeping sensitive information within company-controlled environments.
This is a senior individual-contributor role spanning applied LLM engineering, platform architecture, search and data pipelines, security, and production operations. You will turn promising prototypes into a dependable internal capability: selecting and optimizing open-weight models, building permission-aware retrieval, creating reusable APIs and tools, integrating with existing engineering workflows, and establishing objective ways to measure quality, safety, latency, capacity, and business value.
The successful candidate will understand that a useful enterprise LLM is more than a model and a chat interface. It requires trustworthy source grounding, strong access controls, repeatable evaluation, careful tool permissions, observable production services, and an operating model that keeps data, indexes, prompts, models, and dependencies current. You will make pragmatic build-versus-buy decisions and choose the simplest approach-search, retrieval-augmented generation (RAG), prompting, workflow automation, or model adaptation-that meets each use case.
Initial use cases may include engineering knowledge discovery, source-code understanding, troubleshooting assistance, technical-document Q&A and summarization, test and log analysis, and drafting structured engineering artifacts. The platform should be extensible to additional approved use cases as needs and model capabilities evolve.

As an Engineering Manager in the data department, youll build and scale our data platform and data apps that powers our business insights. Youll design and implement robust pipelines to process billions of daily records, leveraging cutting-edge cloud technologies to transform data into actionable intelligence.

If youre passionate about data engineering and driving business growth through insights, wed love to hear from you!

Responsibilities
Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
Architect and scale batch and streaming pipelines powering core business and ML use cases.
Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
Ensure reliability, scalability, and observability of large-scale data and ML systems in production.

Were hiring a Senior Infrastructure Lead to own the end-to-end planning, deployment, and maintenance of our IT infrastructure - Linux systems, Active Directory & identity management, NetApp storage, and virtualization/cloud environments - while working closely with R&D/DevOps teams and leveraging AI tools for automation and optimization.

Responsibilities:
Design, deploy, and maintain Linux (hands-on) and Windows Server environments.
Manage virtualization (VMware/KVM) and cloud (AWS/Azure) environments.
Own architecture, configuration, and ongoing maintenance of NetApp storage systems (ONTAP/FAS/AFF) - Volumes, Snapshots, Qtrees, and performance optimization.
Own Active Directory architecture and operations - users, permissions, GPOs, Entra ID (Azure AD), and Hybrid sync.
Manage backups, DRP, and data replication.
Write automation scripts (PowerShell, Bash, Python, Ansible).
Provide infrastructure support to R&D/DevOps teams, including DEV/QA/Staging/Production environments (and AI/ML workloads as needed).
Support CI/CD processes and infrastructure architecture.
Leverage AI tools & LLMs (GitHub Copilot, ChatGPT, Claude) to accelerate scripting, automate sysadmin tasks, and enhance troubleshooting.

We are looking for an experienced MCP (Model Context Protocol) Software Engineer to design, develop, and optimize Model Context Protocol implementations. You'll work on building robust MCP servers, client integrations, and tools that allow AI systems to securely access and interact with various data sources and services.

What You'll Do:

MCP Development & Implementation
Design and develop MCP servers for various data sources (databases, APIs, file systems, cloud services).
Build and maintain MCP client integrations for AI applications and platforms.
Implement secure authentication and authorization mechanisms for MCP connections.
Develop custom MCP tools and resources to extend AI capabilities.

Architecture & Design
Architect scalable MCP solutions that handle high-volume data access patterns.
Design efficient protocol implementations that minimize latency and maximize throughput.
Create reusable MCP components and libraries for internal and external use.
Establish best practices for MCP server development and deployment.

Integration & Collaboration
Integrate to our identity platform.
Work with DevOps teams to deploy and monitor MCP services in production environments.
Partner with security teams to ensure MCP implementations meet compliance and security requirements.
Integrate MCP solutions with existing infrastructure and third-party services.

Quality & Performance
Write comprehensive tests for MCP implementations including unit, integration, and end-to-end tests.
Monitor and optimize MCP server performance and resource utilization.
Debug complex issues in distributed MCP environments.
Maintain detailed documentation for MCP servers and integration patterns.

Contribute to Open Source
Engage with the open-source community, contributing to the development and improvement of MCP.

Where You'll Be: Tel Aviv | Full-time, hybrid.

We're looking for a Head of AI to own everything AI and ML at Sensos - from the agentic systems that power autonomous supply chain execution to the data science and modeling that turn our real-world data into an unfair advantage.
This is a founding leadership role. You'll be 100% hands-on from day one - researching, prototyping, and shipping models and AI capabilities directly into the platform - and over time you'll build and lead the data organization (data science, ML, and analytics). Put simply: you're the person who brings the "smart" into the platform.
We capture billions of real-world signals from IoT smart labels moving through global supply chains. That data is the raw material; your job is to turn it into prediction, detection, and trustworthy autonomous decisions.
What You'll Do:
Work with a wide variety of datasets - IoT sensor, GIS, time-series, tabular, and textual
Solve a range of business problems: time-series prediction, anomaly detection, scoring, and agentic flows
Design real-time inference pipelines
Develop and deploy ML models and LLM-based agents that serve tens of enterprise clients
Conduct exploratory data analysis, feature development, and correlation analysis
Take work all the way from prototype to production - you own it end-to-end
Partner closely with Product and Engineering to embed intelligence natively into the platform and shape the roadmap
Stand up the infrastructure: evaluation pipelines, model promotion, MLOps, and artifact management
Build, hire, and lead the data function (DS, ML, and analytics) as we scale
Lead patent registrations, technical presentations, and stakeholder discussions

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 are looking for a Staff Architect, Data & AI Infra to shape, build, and scale the infrastructure that powers our data, AI, and research platforms. This is a senior player-coach role with broad architectural ownership across data infrastructure, ML infrastructure, developer experience, reproducibility, and production reliability. You will work across the wider engineering group as a hands-on technical architect, while also managing a small team of individual contributors focused on ML infrastructure.

This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make our data and AI platforms scalable, reproducible, secure, compliant, and easier to use.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Architectural Leadership: Own and evolve the technical roadmap for our data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future.
MLOps & Platform Development: Design and build end-to-end MLOps systems-covering experimentation, training, reproducibility, and deployment-while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads.
Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails.
Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction.
Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group.
Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes.

We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying our AI models. You will own and evolve our benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.

This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models.

Location: Ramat Gan, Israel (hybrid model)

What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.