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We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership.
As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level.
You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers.
Responsibilities
Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies.
Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph.
System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks.
Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment.

we are the AI-native platform powering the era of Agentic HR. Powered by Loomra, our context engine is purpose-built for the workforce. our company lets you build any Agentic HR agent that operates in the flow of work across Slack, Microsoft Teams, Copilot, and Google Chat. Talent Redeployment, internal mobility, skills intelligence, workforce redesign, and whatever comes next.
Trusted by leading global enterprises, our company serves over 1.5 million employees across 30+ Fortune 500 companies, unlocking more than 4.8 million strategic work hours. We help organizations harness AI to unlock agility, productivity, and growth, empowering them to achieve true ROI and drive exponential productivity.
About the Role
We are seeking an experienced and passionate Senior Software Engineer to join our core engineering team. In this role, you will design, build, and scale high-performance backend services that power our enterprise Agentic AI platform. You will tackle complex technical challenges, architect robust systems, and work on cutting-edge features that seamlessly integrate AI agents into enterprise workflows.
As a Senior Software Engineer at our company, you will collaborate closely with product managers, and cross-functional engineering teams. You will take full ownership of end-to-end feature delivery from initial system design and API definition to production monitoring and optimization - ensuring our platform maintains top-tier reliability, security, and execution velocity.
Responsibilities
Design, architect, and maintain highly scalable, high-throughput backend services and distributed systems.
Take end-to-end ownership of features and projects, driving them from technical design through to deployment and monitoring.
Build and optimize core APIs and integration layers.
Write clean, testable, and maintainable code, adhering to software engineering best practices (CI/CD, code reviews, automated testing).
Drive architectural decisions, lead technical design discussions, and advocate for system performance and scalability improvements.
Troubleshoot and resolve complex production issues, ensuring high system availability and low latency.
Mentor and guide junior and mid-level software engineers, driving engineering excellence across the team.

Own the technical integrations that connect us with retailers systems, making sure data flows are smooth, secure, and ready for scale.

Responsibilities
Manage data integrations with POS, ERP, and other systems.
Define and document technical requirements for new onboardings.
Work with APIs, SFTP, and database connections.
Troubleshoot and resolve integration issues with engineering.
Collaborate with retailers IT teams during setup.

At our company, everyone builds. We don't separate the people who decide from the people who ship - the fastest way to earn trust here is to make something and put it in front of customers. We move with urgency because fast is respect: for buyers, for teammates, and for the size of the opportunity in front of us. We're creating the Agentic HR category, not competing in an existing one, which means we'd rather run an honest experiment than recycle a best practice. We're direct about what's working and what isn't, allergic to bureaucracy and status games, and looking for people who lean in - not lean back.
We're looking for an AI Growth Marketing Lead to build the demand engine for our Agentic HR platform from the ground up. We're creating a new category, and you'll define what an AI-native growth motion actually looks like: not by directing it, but by building it yourself with the best tools available, and owning the pipeline number it generates.
In your day-to-day, you'll run the full path across our mid-market and enterprise motions - paid, organic, content, email, webinars, ABM, events, and executive roundtables - while designing and shipping the AI-powered workflows that make it all run: intent-triggered outbound in Clay, agents in Claude and n8n, pages and campaigns you spin up yourself instead of waiting on anyone. You'll work closely with Sales, Alliances, and Product, and have unusual latitude to test, ship, and scale what works.
One thing to be clear about: this is not a marketing ops or tools-configuration role. The tools are your leverage. Marketing judgment, including knowing what moves an enterprise buyer, what a healthy funnel costs, and which experiments deserve to live, is the job.
Responsibilities
In this role you'll
Own the pipeline generation number and the growth strategy behind it: targets, channel mix, budget allocation, and the experimental framework to hit them
Design and run the full demand generation mix, tuned to the different rhythms of mid-market and enterprise buying cycles
Build the AI-native motions displacing traditional spend: intent-triggered outbound, AEO/GEO, signal-based plays, and agent-run workflows that work while you sleep
Run a disciplined test-and-iterate loop across channels, messaging, offers, and audiences - compounding what works, cutting what doesn't
Design, build, and maintain the workflows that run the growth motion, hands-on in Clay, Claude, n8n, Qualified, HubSpot, and Salesforce, and continually adopting what's next
Define the metrics that matter, build the reporting cadence, and present pipeline performance and spend efficiency to leadership
Work shoulder-to-shoulder with our C-suite to build and scale our growth engine - no layers, no red tape. Your growth experiments shape company strategy from day one
Partner with sales and product to keep messaging tight and conversion high at every touchpoint.

Required Quality & Reliability Lead
What we are looking for:
The Quality & Reliability Engineer is responsible for driving product quality, manufacturing excellence, and reliability performance across our products and operations.
Working closely with Engineering, Manufacturing, Supply Chain, Service, and R&D teams, this role leads quality and reliability initiatives that improve process capability, manufacturing yields, product robustness, and customer satisfaction through data-driven decision making, structured problem solving, and continuous improvement methodologies.
The role is also expected to leverage AI and advanced analytics tools to accelerate insights, improve decision making, and enhance operational effectiveness.
Responsibilities
What will you be doing:
Responsibilities - Quality:
Lead quality improvement initiatives focused on yield enhancement, defect reduction, process stability, and continuous improvement across DMD operations.
Establish and maintain Statistical Process Control (SPC) and drive process capability analysis using statistical methods and data-driven quality metrics.
Lead root cause investigations utilizing methodologies such as 8D, 5 Why, Fishbone Analysis, Pareto Analysis, DOE, and FMEA.
Drive NCR, CAPA, and corrective action processes, identify trends, prioritize actions using evidence-based analysis, and verify effectiveness of implemented solutions.
Partner with Manufacturing, Engineering, Service, and Customer Support teams to resolve product quality issues, customer complaints, field incidents, and escalations.
Collaborate with suppliers to improve quality performance, drive supplier corrective actions, and reduce supplier-related quality risks.
Conduct supplier audits and assessments, including evaluation of manufacturing controls, process capability, inspection methods, and process discipline.
Review supplier quality documentation including CoC, CoT, ATR, inspection records, and quality reports to identify risks, trends, and improvement opportunities.
Lead internal and external audits, ensuring compliance and effective corrective actions.
Responsibilities - Reliability:
Partner with Engineering and R&D teams to define reliability qualification plans, test strategies, acceptance criteria, and reliability requirements for new and existing products.
Ensure reliability testing programs are properly executed and that test results are systematically reviewed, analyzed, and translated into actionable improvements.
Partner with Service and analyze reliability, field performance, MTBF, and MTTR data to identify trends, emerging risks, and improvement opportunities.
Lead investigations of reliability failures and support identification of physical, mechanical, electrical, optical, material, and process-related failure mechanisms.

Help our retail customers adopt us, understand its AI capabilities, and turn it into a daily tool for their teams.

Responsibilities
Onboard and train new retail customers.
Guide clients in using our AI features and workflows.
Monitor usage and identify opportunities to increase engagement.
Support day-to-day questions from operations teams.
Act as the bridge between customers and the product team.

15,000 NIS/mo + bonuses.

Every camera and sensing system we ship is used, every day, by hundreds of millions of people who simply expect it to work. Ensuring our systems are worthy of that expectation is this team's goal. We challenge products through the physical world to prove it, and to feed real-world evidence back into system design - measuring and analyzing the performance of multi-disciplinary optical sensing systems. This role is the analytical core of that work. You will define what we measure and why, mine large volumes of measurement data for the effect that actually matters, and take a question that has never been answered to a defensible answer. It is highly collaborative: you will work directly with module, optics, algorithm and system teams, and your conclusions will be the basis of their decisions. You will work alongside our lead system engineer as a peer, with problems allocated between you according to where the issue actually sits. We are looking for someone who genuinely enjoys digging into data until it gives up what it knows, and who wants to take that on with us.
Responsibilities
Own the characterization of system performance end to end - from risk and failure mode mapping through methodology definition, execution, analysis and reporting
Define metrics, testing & measurement methodology
Mine large data sets to separate isolate performance root causes
Correlate physical measurement to human perception - where the HW meets the user
Build the analysis capability the work requires - pipelines, tooling methods
Present and defend findings to module, optics, algorithm and system teams