Job Opportunities API

⚡ Senior AI Workflow & Systems Engineer   Build and run the AI infrastructure that powers every team at TubeScience. 🗃️ Role: Senior AI Workflow & Systems Engineer   📍 Location: Remote (Los Angeles based preferred)   💰 Compensation: Remote $70,000–$120,000 | Los Angeles $110,000–$160,000   👤 Reports to: VP of IS   🏢 Team: Information Systems 🚀 About TubeScience TubeScience is a data-driven creative studio producing performance advertising at massive scale — and we're growing fast. We're looking for a Senior AI Workflow & Systems Engineer to be the most technically sophisticated AI builder in the company. You'll sit in IT but serve everyone — owning the infrastructure, deployments, and systems that make our AI initiatives real, and unblocking every team that's building on top of them. 💡 The Role This is a systems and deployment role for someone genuinely excited about where AI is taking enterprise engineering. You won't just design workflows — you'll own the infrastructure they run on, keep them running reliably, and be the expert other teams call when things break or they hit a wall. You are the architect, the deployer, the maintainer, and the unlocker — all in one. When there's no PM driving an AI initiative, you'll step in and own it end-to-end. 🎬 What You'll Own 🤖 AI Workflow Engineering - Build and deploy LLM-powered applications and agent-based workflows that eliminate manual effort across the company - Design multi-step agentic pipelines — tool use, RAG, structured outputs — built for production, not demos - Integrate AI workflows with TubeScience's existing systems via REST APIs, webhooks, and custom integrations - Develop automation pipelines - Evaluate emerging AI tooling and own build-vs-buy decisions 🏗️ Infrastructure & Deployment - Own deployment and management of AI workflows and applications on Vercel and cloud platforms - Build and maintain the infrastructure that supports TubeScience's AI initiatives — including cloud-based agents, serverless functions, and supporting services - Design for resilience: logging, error handling, alerting, and monitoring across all deployed systems - Manage secrets, environment configs, and deployment pipelines across environments - Align with engineering on architecture, scalability, and infrastructure decisions 🤝 Cross-Functional Enablement - Serve as the go-to technical resource for teams across TubeScience building AI-powered workflows and apps - Deploy, maintain, and improve departmental AI tools — owning the full lifecycle from build to production - Debug and unstick builders across the company when they hit technical walls - Translate team-specific business needs into precise technical requirements and actionable solutions - Serve as final escalation for complex AI and systems issues teams can't resolve on their own 🔬 Ownership & Improvement - Proactively audit AI systems and workflows for reliability issues, inefficiencies, and improvement opportunities - When there's no dedicated PM on an AI initiative, step in: define the problem, scope the solution, and drive it to completion - Prototype emerging AI tools and frameworks and bring the best ones into TubeScience's stack - Document every system thoroughly so the company can run it confidently 🧬 What We're Looking For Background & Experience - 4–6+ years in software engineering, DevOps, or systems engineering — with hands-on AI/ML experience - Strong foundation as a software, systems, or DevOps engineer who has grown into AI — not the other way around - Proven experience deploying and managing production applications on Vercel, AWS, GCP, or equivalent - Hands-on with LLMs, generative AI, and orchestration tools (n8n, Make, Zapier, LangChain, or equivalent) - Proven REST API integration experience with solid edge-case handling - Experience building or maintaining cloud-based agents