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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Lead Manager (TLM) - AI Systems & Agents - **Company:** Instabase - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $59,800.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Database, Computer Engineering, Directed Acyclic Graph (Directed Graphs), Python (Programming Language), PostgreSQL, Node.Js, Redis, Next.js, Software Engineering, Google Cloud, ReactJS, Large Language Models, Multi-Agent Systems, State Machines, Caching, Backend, Kubernetes, Software Coding, Stream Processing, Docker - **Published:** July 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7045fce9ccd205ca ## About the Role * 8+ Years of Professional Engineering Experience: A strong, proven background in software engineering, with significant tenure building and scaling backend systems, infrastructure, or platform services. * Active LLM & Agentic Engineering Experience: Practical, hands-on experience building production-grade LLM applications, multi-agent workflows (using DAGs or state machines), or tool-calling executors. * Systems & Infrastructure Rigor: Deep familiarity with building secure sandboxed runtimes, container isolation, API platforms, caching strategies, or high-throughput real-time data streaming. * Small-Team Leadership Experience: Proven track record of mentoring senior engineers, driving technical design reviews, and leading high-velocity squads as a Tech Lead, TLM, or hands-on EM. * An Active Builder: You love writing code. You are excited about the prospect of coding daily and staying out of the meetings and administrative bloat of traditional big-tech management. * Educational Background: BS, MS, or Ph.D. in Computer Science, Computer Engineering, or a highly quantitative field. ## Description * 60% to 70% Hands-on Engineering: Actively designing multi-turn agent loops, writing production code, building secure sandboxing environments, and architecting systems. * 30% to 40% Leadership & Mentorship: Recruiting top-tier talent, mentoring engineers, reviewing PRs, and aligning product roadmaps with technical execution., * Architect Stateful Agent Loops: Design and implement highly reliable, multi-turn agentic planning and execution state machines capable of self-correction, tool orchestration, and long-context memory persistence. * Build Secure Execution Sandboxes: Own the runtime execution layer where model-generated code (such as Python data-analysis scripts) is safely isolated, monitored, and run in real-time without compromising enterprise security boundaries. * Pioneer Tool Orchestration & MCP: Expand our agent capabilities by designing composable tool-calling integrations using Model Context Protocol (MCP) and custom API interfaces. * Build Evaluation & Trajectory Testing Harnesses: Establish programmatic frameworks (using LLM-as-a-judge and trajectory evaluations) to test if agent loops, tool-calling selections, and error recoveries behave safely and deterministically. * Scale High-Performance Systems: Work on deep systems problems including latency optimization, semantic caching, speculative execution, and streaming intermediate agent states for a responsive user experience. * Scale the Team: Bootstrap, hire, and mentor a small, elite, high-velocity team of software and AI engineers., * Infrastructure & Security: Secure sandboxed runtimes (Docker, gVisor, WebAssembly), GCP, AWS, Kubernetes, Cloud SQL (PostgreSQL, TimescaleDB), Redis, Pub/Sub. * AI & Models: Integration with advanced reasoning models (GPT-4o, o3, Claude 3.5 Sonnet, DeepSeek) across custom function-calling, LLM fine-tuning, and hybrid vector-search RAG pipelines. * Standards: Model Context Protocol (MCP) tool schemas and APIs. ## Related Videos - [One AI API to Power Them All](https://www.wearedevelopers.com/videos/1601-one-ai-api-to-power-them-all) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Beyond Chatbots: How to build Agentic AI systems](https://www.wearedevelopers.com/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)