Principal AI Engineer, Media Pipelines

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal AI Engineer, Media Pipelines based in the United States.

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.

Accountabilities:

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:

• Pipeline Architecture: Define the architecture for media ingestion, transformation, quality assurance, and delivery. Make key decisions about orchestration, data models, service boundaries, and system design, documenting them clearly for other engineers.
• Model and Vendor Strategy: Evaluate and select technologies across large language models, speech recognition, text-to-speech, translation, and video processing. Conduct structured model comparisons and determine when to build, purchase, or fine-tune solutions.
• Flexible AI Infrastructure: Design modular pipelines that allow AI models and service providers to be replaced or upgraded without requiring major architectural changes.
• Cost, Latency, and Throughput Optimization: Own the cost per minute of processed media and improve efficiency through batching, caching, intelligent routing, and GPU resource management. Ensure the system degrades gracefully when external providers experience delays or outages.
• Evaluation and Quality Assurance: Develop evaluation frameworks for translation accuracy, dubbing naturalness and synchronization, clip selection, and brand compliance. Implement automated quality gates, verification checks, silence detection, and failure detection, supported by targeted human review.
• Enterprise Reliability: Establish service-level objectives (SLOs), observability practices, incident response procedures, and robust data-handling standards for sensitive, unreleased, or rights-restricted client content.
• Rights, Consent, and AI Safety: Collaborate with leadership to address voice-cloning consent, likeness rights, content ownership, and AI disclosure, embedding relevant safeguards into the system architecture.
• Engineering Standards and Mentorship: Set expectations for design documentation, code reviews, testing, and technical quality. Support other engineers through mentoring, pairing, and constructive feedback.
• Hands-On Product Delivery: Personally develop critical components and lead high-priority roadmap initiatives from initial prototypes through production deployment.
• Continuous Improvement: Identify scalability bottlenecks, assess how the architecture will perform at ten times current volume, and implement improvements that increase efficiency, quality, and operational maturity.
• Measurable Outcomes: Reduce processing costs, establish trackable quality scores for every output type, accelerate the introduction of new models and languages, and deliver transparent reliability reporting to enterprise customers.

Requirements:

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.

• Extensive Engineering Experience: Typically 10+ years of software engineering experience, includin…