Senior AI Software Engineer
Placerlabs · United States, Remote
About this role
**ABOUT PLACER.AI** Placer.ai is transforming how organizations understand the physical world. Our location analytics platform provides visibility into locations, markets, and consumer behavior—helping customers make smarter, data-driven decisions. We’ve built an advanced location intelligence platform with an uncompromising commitment to privacy. We’re growing fast (including reaching **$100M ARR in 6 years** and achieving **unicorn status** in 2022) and are creating a **$100B+ market opportunity**. --- **SUMMARY** Placer.ai is building **PlacerX**—the AI agentic and context layer we use to run the company internally. This includes the tooling, integrations, agents, and automation that make every team faster. As a **Senior AI Software Engineer**, you’ll help build this platform end to end: agents, connectors, automation, and infrastructure that unlock major productivity gains across the business. You’ll also drive deeper integrations and move from surface-level usage to genuinely **agentic, high-leverage workflows** embedded across teams. This is a **hybrid role** (platform engineering + internal-facing integration lead). You’ll report to the COO and partner across AI Operations, R&D, Data Science, GTM, and more—owning the path from integration request to production-grade systems. --- **RESPONSIBILITIES** - **Agent Design & Orchestration**: Architect agents that *do work* via multi-step workflows (reasoning, tool calls, decision-making), with planning/control logic for autonomous loops or human-in-the-loop. - **Tool & Context Layer (incl. MCP Servers)**: Provide secure, governed access to systems/data so agents can operate across finance, GTM, Ops, and R&D. Build and deploy MCP servers for: - External SaaS integrations (e.g., Google Analytics, Search Console, Datawrapper, Infogram) - Fully custom MCPs for Placer internal tools (end-to-end from data source to Cowork plugin surface) - **MCP Server Development**: Design, build, and deploy MCP servers across both external and internal tracks. - **Auth Infrastructure**: Build OAuth + credential management for connector auth at scale, using reusable patterns others can adopt. - **Connector Standards**: Define “production-ready” requirements (security posture, k8s deployment target, logging, access controls) and apply them consistently. - **Integration Triage & Prioritization**: Triage connector requests and bug reports; distinguish rollout-critical vs. backlog and help teams self-serve where possible. - **Plugin & Skill Development**: Build/maintain Cowork plugins and Claude skills to extend Claude for specific Placer workflows (especially Marketing, Operations, Data). - **Data Platform Engineering (Databricks / Internal BI)**: Own and improve the Databricks BI environment—cleaning/restructuring data, building pipelines, and making the data AI-ready. - **Usage Tracking & Insights**: Build tooling to track internal AI tool usage for adoption measurement and investment decisions. - **Platform Infrastructure & Cost Optimization**: Optimize compute/storage/networking for performance and cost efficiency. - **Security & Compliance**: Implement security best practices (identity management, encryption, compliance monitoring) to ensure AI access is safe and auditable. --- **REQUIREMENTS** - **8+ years** of backend engineering experience - Experience with **MCP servers**, **LLM tool use**, or **AI agent frameworks** - Experience in **data engineering** or **analytics tooling** - Strong u
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