Gloat

Description Gloat is the AI-native platform powering the era of Agentic HR. Powered by Loomra, our context engine is purpose-built for the workforce. Gloat 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, Gloat 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 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. Requirements Must-Have: • 2+ years of experience managing or leading a high-performing software/AI engineering team. • 5+ years of hands-on experience as a Backend or AI/Software Engineer building complex, scalable distributed systems in production. • Proven hands-on experience building and shipping LLM agents to production - not just demos or prototypes. • Hands-on with at least one agent orchestration framework (e.g. LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar). • Strong Python and solid software engineering fundamentals. • Strong agile project management skills with a track record of effective sprint planning and execution. • Demonstrated ability to thrive in a dynamic, high-pressure startup environment with rapid execution and frequent shifts in direction. • Excellent communication skills, with the ability to build strong partnerships with Product Managers and other stakeholders. Nice-to-Have: • 2+ years hands-on with LLMs / generative AI. • Practical experience with RAG, embeddings, and vector databases (e.g. pgvector, Pinecone, or similar). • Experience with tool use / function calling and integrating LLMs with external systems. • Experience with MCP, agent memory, and planning/reasoning patterns. • Responsible AI: bias evaluation, guardrails, and AI governance.

Description At Gloat, 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 Requirements • 4+ years in growth marketing or demand generation at a B2B SaaS or AI-native company, with a track record of owning a pipeline number • Hands-on experience running both mid-market and enterprise demand motions, and comfort with the different tactics each requires • Real mileage with AI-native tech - you've built agentic workflows (Claude, n8n, Gumloop, Lindy, or similar), enriched and triggered outbound with Clay, vibe-coded in Lovable or Replit, tested AI SDRs, and have opinions about what's real vs. demo-ware • Hands-on Salesforce and HubSpot experience • Funnel fluency and strong analytical skills: you create, track, and report on the metrics that matter • Excellent communication skills, with comfort creating content and positioning in a technical, category-defining space • Experience marketing a genuinely new category or an agentic/AI product (preferred)

Description Gloat is the AI-native system of action for workforce orchestration. Our agentic HR platform lets enterprises build intelligent workforce agents that understand their people, respect their policies, and work where teams already are — Microsoft Teams, Slack, Google Chat, and Copilot, with no new portal or change management. The platform is powered by Loomra, our semantic layer that connects people, jobs, and skills through a knowledge graph, deep HCM integration (Workday, SuccessFactors, Oracle HCM), a governance engine, and semantic retrieval — with a full audit trail behind every agent decision. Trusted by 100+ enterprises across 112 countries, empowering 5M+ employees. We're looking for a smart and capable DevOps Engineer. You will be part of a small and agile team, working on well-built infrastructure, handling production workloads, building and maintaining infrastructure that supports continuous delivery. This job includes work across the devops stack, including agent development, CI/CD, networking, resource allocation, cloud architecture, security, developer experience, and more. We work with enterprise customers, with the SLA and expectations that this dictates. This position includes on-call shifts. Our stack: Kubernetes on AWS Terraform and IAC Github & Github Actions Responsibilities What will you DO: • Design and evolve our cloud multi-region architecture on AWS - networking, interconnects, IAM and high-availability topology - and express it entirely as code with Terraform, following GitOps as a first principle. • Build and own the CI/CD pipelines that plan, review, test and safely apply IaC changes - Policy-as-Code guardrails, drift detection and progressive rollout so infrastructure changes ship as confidently as application code. • Advance Platform-as-a-Product: build self-serve capabilities and paved paths so engineers can provision what they need • Strengthen our observability stack - metrics, logs, traces and alerting. • Troubleshoot and mitigate production incidents, participating in on-call rotations to ensure system health and stability. • Design and develop the next generation of AI agents to support higher pace and evolve the RnD productivity while maintaining security and engage the entire company to use AI for better solutions. Requirements ・Minimum 5 years of experience in DevOps or related roles, with exposure to cloud infrastructure and Kubernetes ・Working knowledge of at least one major cloud platform (AWS, GCP, or Azure) ・Experience with containerization technologies, especially Docker and Kubernetes ・Understanding of CI/CD principles with experience using Github or similar ・Hands-on experience with infrastructure as code (Terraform, Ansible, or similar) ・Strong problem-solving skills and ability to troubleshoot in a distributed environment ・Experience in developing agents for internal use (Developer Agent, Devops Agents etc) ・Good communication skills, able to work with cross-functional teams ・English proficiency (written and verbal)

Description Gloat is the AI-native system of action for workforce orchestration. Our agentic HR platform lets enterprises build intelligent workforce agents that understand their people, respect their policies, and work where teams already are — Microsoft Teams, Slack, Google Chat, and Copilot, with no new portal or change management. The platform is powered by Loomra, our semantic layer that connects people, jobs, and skills through a knowledge graph, deep HCM integration (Workday, SuccessFactors, Oracle HCM), a governance engine, and semantic retrieval — with a full audit trail behind every agent decision. Trusted by 100+ enterprises across 112 countries, empowering 5M+ employees. This role sits at the center of that effort. We're looking for a Senior AI Engineer to design, build, and ship production-grade LLM agents that reason over workforce and skills data on top of Loomra's semantic layer. You won't just prototype — you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily (Teams, Slack, Copilot), and iteration in front of enterprise customers. You'll work alongside product, data, and platform engineers to turn powerful capabilities into reliable, safe, and fast product experiences. If you've built agents that actually made it to production — and you care as much about evaluation, guardrails, and reliability as you do about capability — we want to talk to you. Responsibilities • Design and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents. • Implement retrieval and RAG pipelines over structured and unstructured workforce data, grounded in our knowledge graph connecting people, jobs, and skills. • Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows. • Build evaluation harnesses, guardrails, and safety/bias checks, and work within the governance engine so agents behave reliably, respect customer policies, and produce a full audit trail. • Ship agents in a model-agnostic way across providers (Anthropic, Google, IBM watsonx) and deploy them into Teams, Slack, and Copilot. • Optimize agents for latency, cost, and reliability at enterprise scale. • Take agents from prototype to production — with monitoring, observability, and a fast iteration loop. • Partner closely with product, data, and platform teams to translate customer needs into agent capabilities. Requirements • 5+ years building production software • Proven experience building and shipping LLM agents to production — not just demos or prototypes. • Hands-on with at least one agent orchestration framework (e.g. LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar). • Strong Python and solid software engineering fundamentals. • Prompt engineering paired with systematic, measurable evaluation of LLM outputs. • Experience with tool use / function calling and integrating LLMs with external systems. • Track record deploying, monitoring, and maintaining AI in production (cloud, CI/CD, observability). Nice to Have • 2+ years hands-on with LLMs / generative AI. • Practical experience with RAG, embeddings, and vector databases (e.g. pgvector, Pinecone, or similar) • Experience with MCP, agent memory, and planning/reasoning patterns. • Background in HR tech, people data, or skills ontologies. • Knowledge graph / graph ML experience (knowledge graphs, GNNs). • Responsible AI: bias evaluation, guardrails, and AI governance. • Experience working across multiple model providers (e.g. Anthropic, Google, IBM watsonx) rather than a single vendor.

Description Gloat is the AI-native platform powering the era of Agentic HR. Powered by Loomra, our context engine is purpose-built for the workforce. Gloat 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, Gloat 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 Gloat, 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. Requirements Must-Have: • 7+ years of hands-on experience as a Backend or Full-Stack Software Engineer building complex, scalable distributed systems in production. • Deep technical proficiency in modern backend languages (e.g., Python, Node.js / TypeScript, Go, or Java). • Practical experience building or integrating AI agent frameworks, LLM applications, or vector databases. • Proven ability to execute and deliver high-quality software at a fast pace, specifically within an agentic or highly dynamic AI-driven environment. • Strong expertise in microservices architecture, RESTful APIs, and message-driven architectures (e.g., Kafka). • Hands-on experience with both relational databases (e.g., PostgreSQL) and NoSQL/caching solutions (e.g., MongoDB, Redis). • Deep understanding of cloud infrastructure (AWS/GCP), containerization (Docker, Kubernetes), and CI/CD pipelines. • Strong architectural vision with a proven track record of bringing technical projects from concept to production deployment. • Outstanding problem-solving skills, ownership mindset, and strong collaborative skills. Nice-to-Have: • Experience building systems for large-scale data integration from complex enterprise platforms (e.g., SAP, Workday). • Experience with Infrastructure as Code (Terraform, Helm, ArgoCD).