Job Description
⚡ 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 and serverless infrastructure
Technical Skills
- Strong Python and/or JavaScript/Node.js — clean, production-grade code
- Solid understanding of deployment pipelines, CI/CD, environment management, and secrets handling
- Experience with vector databases and embedding-based retrieval
- Comfortable with cloud infrastructure (AWS and/or GCP) and cloud-native application patterns
- Familiarity with monitoring, logging, and alerting for production systems
Soft Skills
- Highly autonomous — identifies problems and ships solutions without waiting to be asked
- Effective communicator across technical and non-technical audiences
- Strong product instincts: can step into ownership of an initiative when there’s no PM in the room
- Calm under pressure; reliable when other teams are blocked and need answers fast
- Comfortable working across many different teams and problem domains simultaneously
➕ Bonus Points
- Experience with AI agent frameworks
- Background in high-volume performance advertising, media, or creative production
- Experience with AI in a production context
- Multi-step agentic pipeline design or large-scale workflow orchestration
- Experience with data pipelines or BI tooling
✨ Benefits
🩺 Health, Vision & Dental coverage
🧳 Unlimited PTO
💰 401(k) + Matching
💗 Life Insurance
🤒 Paid Sick Days
👶 Paid Parental Leav
