Principal AI Engineer, Media Pipelines

United StatesRemoteFull-timelead

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Pipeline Architecture: Define the architecture for media ingestion, transformation, quality assurance, and delivery. Make key decisions about orchestration, data models, service boundaries,

Requirements Summary

You are an experienced, product-minded engineer who combines deep systems expertise with practical generative AI experience.

Technical Tools

Machine Learning EngineerData

Take a leading technical role in building AI-powered media transformation systems that help creators and media brands extend the reach of their content across platforms and languages. As a Principal Engineer, you will define the architecture and technical roadmap for pipelines that transform large media catalogs into short-form videos, multilingual dubs, and formats optimized for AI-powered discovery. You will combine hands-on engineering with strategic decision-making across multimodal AI, distributed systems, and cloud infrastructure. A key focus will be improving processing speed, controlling inference costs, and ensuring consistently high-quality outputs at scale. Working closely with AI specialists and cross-functional leaders, you will establish engineering standards and guide critical features from experimentation to production. This is an opportunity to shape a rapidly evolving product while solving complex challenges at the intersection of AI, media, and enterprise reliability.

You will own the technical direction and hands-on delivery of the core media transformation pipeline, ensuring that it remains scalable, cost-efficient, reliable, and adaptable as models and customer needs evolve. Your responsibilities will include:

Requirements

~2 min read

You are an experienced, product-minded engineer who combines deep systems expertise with practical generative AI experience. You are comfortable making high-impact technical decisions in an evolving environment, validating assumptions through experimentation, and taking responsibility for production outcomes.

- Technical ownership: Lead the architecture and technical direction of a core AI product while remaining hands-on with critical engineering work.
- Product impact: Build AI-powered tools that help creators and media brands distribute content across platforms, languages, and AI-driven discovery channels.
- Advanced AI challenges: Work across multimodal AI, model orchestration, speech technologies, video processing, and large-scale distributed systems.
- Engineering autonomy: Influence technology choices, architecture, vendor strategy, and the product roadmap.
- Cross-functional collaboration: Work closely with AI engineering specialists and organizational leaders to translate ambitious product goals into production systems.
- Professional growth: Establish engineering standards, mentor other engineers, and help shape the technical foundations of an evolving AI product.

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