--- title: 'Senior Applied AI Engineer – Enterprise Systems at TubeScience' canonical: 'https://feeny.ai/job/senior-applied-ai-engineer-enterprise-systems-tubescience-los-angeles-california-qa10vbgd0fns' type: 'job' last_seen: '2026-09-08' --- # Senior Applied AI Engineer – Enterprise Systems at TubeScience - **Company:** TubeScience - **Location:** Los Angeles California, United States - **Work type:** remote - **Posted:** 2026-07-24 - **Last confirmed live:** 2026-09-08 - **Apply:** https://job-boards.greenhouse.io/tubescience52/jobs/5195854007 ## Job description Role: Senior Applied AI Engineer – Enterprise Systems Location: Remote (US) or Los Angeles (preferred) Compensation: - Remote: $70,000–$120,000 - Los Angeles: $110,000–$160,000 Reports to: VP of Information Systems (Eilrama) Team: Information Systems ## About TubeScience At TubeScience, we build software systems that combine AI, engineering, and automation to solve complex operational problems at scale. We’re looking for an engineer who has evolved from systems engineering into applied AI—someone who enjoys designing reliable production systems, integrating modern AI capabilities, and owning them in production. This is an internal Forward Deployed Engineering role. Rather than building products for external customers, you’ll work directly with internal stakeholders to identify operational bottlenecks, architect AI-powered solutions, deploy them rapidly, and continuously improve them based on real business needs. This is not an AI research or model-training position. We apply state-of-the-art AI models to solve enterprise problems through software engineering. ## The Role You’ll own the design, implementation, deployment, and operation of AI-powered enterprise systems that automate business processes across the company. Success in this role means building systems that don’t just work—they continue working reliably after deployment. You’ll be responsible for the complete lifecycle of production AI systems, including architecture, deployment, monitoring, debugging, incident response, and continuous improvement. ## What You’ll Do - Design and build production AI applications that automate complex enterprise workflows. - Architect agent-based systems that coordinate LLMs, APIs, internal services, databases, and business logic. - Build reliable orchestration layers that integrate multiple tools and enterprise platforms. - Deploy production-ready AI systems with observability, monitoring, rollback strategies, and operational safeguards. - Investigate production issues, analyze logs, debug failures, and restore system reliability when incidents occur. - Design scalable architectures that prioritize maintainability, resiliency, and operational excellence. - Partner closely with Product, Operations, Creative, Engineering, and Business teams to identify high-impact automation opportunities. - Rapidly prototype, validate, deploy, and iterate solutions based on production performance and business outcomes. - Continuously improve existing AI systems for reliability, speed, and business impact. ## Who You Are We’re looking for systems engineers who naturally evolved into building AI-powered software—not AI hobbyists who recently discovered infrastructure. You likely have: - 3–6+ years of professional software or systems engineering experience. - Experience building and operating production software used by real users or internal business teams. - Strong Python engineering experience. - Experience integrating modern LLMs into production systems using frameworks such as OpenAI, Anthropic, LangGraph, MCP, or similar. - Experience designing systems that coordinate multiple APIs, databases, services, and enterprise applications. - Strong understanding of distributed systems, debugging, logging, monitoring, and production operations. - Experience deploying, operating, troubleshooting, and improving production systems after launch. - Strong architectural thinking with the ability to design complete end-to-end solutions. - Comfort working independently in a fast-paced startup environment. Ideal Background The strongest candidates typically come from backgrounds such as: - Systems Engineering - Platform Engineering - Backend Software Engineering - DevOps / Infrastructure Engineering with significant software development experience - Internal Developer Platforms - Enterprise Systems Engineering They later expanded into Applied AI rather than beginning their careers in AI. Experience at a large technology company building production systems is highly valued. Bonus Experience Experience with any of the following is a plus: - Multi-agent systems - LangGraph, MCP, Temporal, or similar orchestration frameworks - Event-driven architectures - Docker and Kubernetes - AWS, GCP, or Azure - CI/CD pipelines - Observability platforms (Datadog, Grafana, OpenTelemetry, etc.) - Internal developer platforms - Enterprise integrations You’ll Thrive Here If You… - Think in systems instead of individual features. - Enjoy solving operational problems through software engineering. - Like building AI systems that become part of day-to-day business operations. - Care about reliability as much as shipping speed. - Are comfortable owning systems after deployment—not just writing the first version. - Enjoy debugging production incidents and improving system resilience. - Like working directly with internal stakeholders to solve real operational challenges. This Role Probably Isn’t For You If… - Your experience is primarily low-code workflow automation (Zapier, Make, n8n, etc.). - Your background is mainly AI research or model training. - Most of your AI experience comes from prototypes, hackathons, or prompt engineering. - You prefer building proof-of-concepts over operating production systems. - You’re looking for a role focused on developing foundation models. - You prefer infrastructure-only work without building production software. ## Why TubeScience You’ll work on high-impact internal systems where your software is deployed quickly, used daily across the business, and has measurable operational impact. We value engineers who take ownership from architecture through production, iterate rapidly, and continuously improve the systems they build. If you’re excited about applying AI to solve real enterprise problems—and owning those systems long after deployment—we’d love to hear from you ## About TubeScience ## Company Overview - **One-liner**: TubeScience is a performance advertising company that produces and optimizes video ads for platforms like Meta, TikTok, and YouTube on a pure pay-for-performance basis. - **Entity Type**: Private (funding stage not disclosed) - **Headquarters**: Los Angeles, California, United States - **Founded**: 2016 - **Founders**: Moshe M. (CEO & Co-Founder) and a President & Co-Founder (full names not publicly available) ## Core Business - **Primary industry**: Advertising Services / Performance Marketing - **Target customers**: B2B – brands, advertisers, and agencies seeking measurable return on ad spend (ROAS/CPA) across social media platforms - **Mission or purpose**: “The World’s Best-Performing Ads” – delivering creative that drives measurable growth, not just views, through a pay-for-performance model ## Products & Services - **Performance Creative Production**: Full in-house production from concept to delivery in a 100,000 sq ft Los Angeles studio – no third-party agencies, enabling same-day turnaround. - **Performance Media Buying**: Expert campaign management across web, shop, lead, and brand objectives, leveraging insights from $2B in annual managed spend. - **TubeScience Labs (Applied AI)**: An in-house AI team that builds tools for creative intelligence, editing automation, and brand/legal review (Flawless platform). - **Advanced Measurement**: Partnerships with NorthBeam, TripleWhale, Haus, and other attribution platforms to ensure accurate ROI tracking. - **Continuous Optimization**: Weekly creative sprints and data-driven iteration; ads that beat targets earn budget, underperformers cost nothing. ## Market Standing - **Valuation/Market Cap**: Not publicly available - **Key Metric**: $2B annual managed spend on Meta (Facebook/Instagram); 277 employees (+17.8% YoY) - **Notable Investors/Partners**: Partners include NorthBeam, TripleWhale, Haus; no investor names publicly disclosed - **Growth Signals**: Headcount grew ~50 people in the last year; operates in 13 countries (including Mexico, Argentina, Philippines, Peru, Brazil); described as “Meta’s largest creative partner” ## Competitive Advantages - **Pay-for-performance model**: Clients pay only when ads outperform their existing benchmarks – zero production fees for video. - **In-house speed**: Concept-to-launch in a single day, enabled by a 100,000 sq ft studio and proprietary AI tooling. - **Creative intelligence**: Models trained on billions in ad-spend data inform creative strategy, reducing guesswork. - **Scale and data advantage**: $2B annual spend provides early visibility into platform algorithm shifts. ## Strategic Focus - **Scaling AI capabilities**: TubeScience Labs continues to build internal tools for creative automation, brand compliance, and performance prediction. - **Global expansion**: Already in 13 countries, likely extending performance creative services to more international markets. - **Deepening platform expertise**: Beyond Meta, active on TikTok and YouTube, with room to expand into emerging ad platforms. ## Why Work Here - **Culture**: Fast-paced, data-driven environment where creative and engineering teams iterate daily based on real-world performance data. Described as “a creative company that moves at the speed of data.” - **Work model**: Hybrid – most roles are based in the Los Angeles studio, but many positions (e.g., remote editors, growth analysts) are open to U.S. and international remote work. - **Perks**: Catered meals, endless coffee & snacks, state-of-the-art 100,000 sq ft studio with on-site production, talent, and post-production teams. Over 16 benefits and perks mentioned. - **Engineering culture**: TubeScience Labs runs a separate hiring track for senior engineers, researchers, and product builders – a small, senior team shipping creative technology to real users. - **Growth opportunities**: High iteration cadence and exposure to billions in ad-spend data provide rapid learning and career acceleration. ## Sources 1. [tubescience.com](https://tubescience.com) 2. [tubescience.com/careers](https://tubescience.com/careers) 3. [linkedin.com/company/tubescience](https://www.linkedin.com/company/tubescience) 4. [job-boards.greenhouse.io/tubescience52](http://job-boards.greenhouse.io/tubescience52) 5. [theorg.com/org/tubescience](https://theorg.com/org/tubescience) ## Other roles at TubeScience - [Associate Creative Strategist](https://feeny.ai/job/associate-creative-strategist-tubescience-los-angeles-california-7a2m2cmftmbr) — Los Angeles California, United States - [Visual Strategist](https://feeny.ai/job/visual-strategist-tubescience-los-angeles-california-c7qrhc2agfec) — Los Angeles California, United States - [Client Solutions Operations Specialist](https://feeny.ai/job/client-solutions-operations-specialist-tubescience-global-e5q5amghqdc8) — Global - [Lead Editor | Remote - LATAM](https://feeny.ai/job/lead-editor-remote-latam-tubescience-global-5sc8za50548j) — Global - [Creator Lead](https://feeny.ai/job/creator-lead-tubescience-los-angeles-california-yxpef9qq8xvs) — Los Angeles California, United States - [Client Solutions Lead](https://feeny.ai/job/client-solutions-lead-tubescience-los-angeles-california-333xgsrgg02t) — Los Angeles California, United States - [Lead Editor](https://feeny.ai/job/lead-editor-tubescience-los-angeles-california-z8rjnjn879sv) — Los Angeles California, United States - [Analyst (Paid Social)](https://feeny.ai/job/analyst-paid-social-tubescience-global-f0rtce7q89yv) — Global - [Video Editor & Motion Graphics Designer LATAM](https://feeny.ai/job/video-editor-motion-graphics-designer-latam-tubescience-global-2d6petwz7f8s) — Global - [Account Director - Growth Marketing (Paid Social)](https://feeny.ai/job/account-director-growth-marketing-paid-social-tubescience-los-angeles-california-kj4davmfs8p3) — Los Angeles California, United States