TubeScience

Build and manage AI infrastructure and workflows, owning full lifecycle of AI applications including architecture, deployment, monitoring, and maintenance. | 4-6+ years in software or systems engineering with production AI/ML deployment experience, proficiency in Python/Node.js, and cloud platform experience (AWS, GCP, Vercel). | ⚡ 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

Develop and maintain the Flawless platform by adding features, improving functionality, and integrating AI tools across the full stack. | Experience building large-scale SaaS platforms end-to-end, proficiency with modern web technologies, and ability to work on complex distributed systems. | About TubeScience Labs TubeScience Labs is the applied-AI team inside TubeScience — the largest performance-video company in paid social. TubeScience is Meta's largest creative partner and AppLovin's #1 creative partner, producing 8,000+ original ads every month from a 100,000 sq ft Los Angeles studio, backed by a library of 1.6 million+ performance ads and $2B in annual managed ad spend. That makes one of the richest first-party creative-performance datasets anywhere. Labs turns that data — and the playbook behind billions in spend — into frontier AI tools that actually ship. Our products run in production against real creative, real deadlines, and real budgets every day. The tools that graduate internally become products we ship to external clients. About The Role Working as a Principal Software Engineer at TubeScience Labs, you will be making direct contribution to our Flawless platform, which processes advertisement assets and allows creatives to easily control every aspect of the ad production pipeline, from ideation to publication. You will improve and add features, develop proof of concepts for potential new ones, constantly researching the state of the art to ensure we are using the best tools and technologies available. You will be expected to autonomously make implementation decisions, revise plans quickly when needed, while maintaining a close collaboration with Product in order to ensure your efforts are focused on what is most critical at any given time. This role is in-person, in our Los Angeles headquarters. In This Role, You Will - Maintain and improve the Flawless platform: improving existing functionality and adding new ones at very quick pace, going from suggestion to production in a matter of days - Develop new features using innovative tools: you will need to look into potential components, test them, and integrate AI tools and other technologies directly into the platform. - Work across the entire stack: Push into the layers most engineers don't — media pipelines, durable workflows, distributed systems internals — to unlock things that aren't possible at the app layer alone. - Research and keep yourself up to date: constantly look at what is on the market and where the technology frontier is, sharing your findings with the team and coming up with innovative ideas for improving the platform - Shape the architecture: Bring an engineering point of view to how the platform grows — what we build, in what order, and why. You Might Be a Good Fit If You - Have built large-scale SaaS platforms from scratch — making the foundational decisions and living with them — not just joined a mature codebase, and can point to systems you owned end to end. - Have shipped products to real customers, dealt with user feedback and with the technical challenges of scaling. - Worked on large monorepo-based projects using leading web technologies, such as Typescript/Javascript with modern frameworks (React/Vue/Angular) and Python. - Understand the abstractions you build on, not just the API surface — you know what's happening a layer or two down, and you can reason from first principles when the abstraction leaks. - Care about quality while keeping in mind deadlines and priorities. Strong Candidates May Also Have - Developed media-specific platforms, such as CDNs, media ingestion pipelines, editing tools, etc. - Experience integrating AI into production systems, not just with chat interactions, but using vendor APIs and managing permissions, costs, different models and vendors - Experience with DevOps, managing infrastructure and developing CI/CD pipelines - Have real depth below the abstraction layer — at least one low-level language (C, C++, Rust, Zig, or equivalent) — and it informs how you build above it. Annual Salary $120,000–$180,000, depending on experience, with eligibility for a performance bonus. How We're Different Most AI tools never leave the demo. Ours can't hide — they're used by expert teams who notice every miss, against a real P&L, all day, every day. We're a small, senior group of scientists and veteran engineers from leading tech and consumer companies, and we hire deliberately for people who want hard problems, real users from day one, and room to own things end to end. We work in tight loops: an idea can go from a prototype to a product in front of operators and clients in days. We start with unique leverage — proprietary data and the industry-leading playbook behind billions in ad spend — build the tools in-house, prove them in production at massive scale, and then ship the winners beyond TubeScience. Velocity beats perfection until something earns the right to scale. Come work with us! TubeScience Labs is the applied-AI team inside TubeScience, headquartered in Los Angeles. You'll join a small, senior group with an outsized surface area: real autonomy, a one-of-a-kind dataset, and a direct line from your code to real customers. We offer competitive compensation, comprehensive benefits, plus generous access to frontier and open-weight models for day-to-day work, prototyping, and evals. Most of all, we offer the rare thing in applied AI: hard problems, expert users from day one, and the room to take an idea from prototype to shipped product. Come build at the frontier with us! We encourage you to apply even if you don't believe you meet every single qualification. Not all strong candidates will check every box as listed, and the best people we've hired rarely did. If this work excites you and you think you could do it well, we'd rather hear from you than have you rule yourself out — so please submit an application.