Principal AI Engineer, Media Pipelines

New

Based in the United StatesFull-TimePrincipal

Salary not disclosed

Apply NowOpens the employer's application page

Job Details

Typically 10+ years of software engineering experience, including ownership of production systems with real users, uptime commitments, and cost constraints.
Demonstrated experience delivering LLM-powered or multimodal AI systems to production, including agentic workflows, multi-step pipelines, or retrieval-augmented generation.
Experience with production evaluation frameworks, retries, failure handling, monitoring, and inference-cost management.
Deep proficiency in Python and/or TypeScript.
Experience using at least one major cloud platform.
Strong understanding of job orchestration, message queues, storage systems, databases, and large-scale workloads designed to recover from failures.
Experience setting architectural direction for systems used by other engineers, supported by design documentation, code reviews, and mentorship.
Ability to make technology choices based on technical and business requirements and explain trade-offs.
Ability to prioritize independently in a remote environment, own outcomes, and keep stakeholders informed.
Advantageous: familiarity with FFmpeg, codecs, containers, transcoding, loudness normalization, and caption or subtitle formats.
Advantageous: experience with speech recognition, text-to-speech, voice cloning, speaker diarization, or lip-sync technologies.
Advantageous: experience self-hosting open-source AI models on GPUs, including capacity planning and cost optimization.

Define the architecture for media ingestion, transformation, quality assurance, and delivery.
Evaluate and select technologies for language models, speech recognition, text-to-speech, translation, and video processing.
Design modular pipelines that support upgrading or replacing AI models and service providers.
Optimize processing costs, latency, and throughput using batching, caching, intelligent routing, and GPU resource management.
Develop evaluation frameworks and automated quality gates for translation, dubbing, clip selection, and brand compliance.
Establish service-level objectives, observability practices, incident response procedures, and data-handling standards.
Embed safeguards for voice-cloning consent, likeness rights, content ownership, and AI disclosure into system architecture.
Set engineering standards for documentation, code reviews, testing, and technical quality, and mentor other engineers.
Develop critical components and lead roadmap initiatives from prototypes through production deployment.

View Full Description & ApplyYou'll be redirected to the employer's site