Principal AI Engineer, Media Pipelines

Remote (United States)

Job Details

Location: United States

Workplace: Remote

Employment Type: Full-time

Experience: Typically 10+ years of software engineering experience, including ownership of production systems

Core Areas: Generative AI, Multimodal AI, AI Pipeline Architecture, LLM Orchestration, Speech and Video Models, Model Evaluation, Distributed Systems, Inference Optimization

Compensation: $175,000-$200,000 annually, plus equity

About the Role

This opportunity is for a Principal AI Engineer, Media Pipelines to lead the architecture, technical direction, and hands-on development of production AI systems that transform video, audio, and text catalogs into short-form clips, multilingual dubbed content, and AI-discoverable media formats. The role focuses on orchestrating generative and multimodal AI models, designing scalable processing pipelines, improving inference efficiency, and establishing automated evaluation and quality controls.

The position combines senior individual contributor responsibilities with technical leadership across LLMs, speech recognition, text-to-speech, translation, video understanding, and distributed systems. Working closely with AI engineering and cross-functional leads, the engineer will define architectural standards, evaluate model providers, improve system reliability, and deliver production features for enterprise media workflows involving sensitive and rights-restricted content.

What You'll Do

• Own the technical architecture of AI-powered media processing pipelines spanning content ingestion, transformation, quality control, and delivery.
• Design scalable orchestration systems that coordinate LLMs, speech recognition, text-to-speech, translation, and video models across large media catalogs.
• Define service boundaries, data models, architectural standards, and technical design documentation that enable reliable system development and integration.
• Evaluate generative and multimodal AI models, including commercial APIs and open-source alternatives, to determine when to build, buy, integrate, or fine-tune capabilities.
• Conduct structured model benchmarks and comparative evaluations to assess output quality, performance, reliability, latency, and cost before production deployment.
• Build modular AI pipelines that support model and vendor substitution without extensive architectural changes.
• Optimize inference costs, processing latency, and throughput through batching, caching, intelligent model routing, and efficient GPU and API utilization.
• Manage processing economics at the cost-per-minute level while maintaining quality and performance across large-scale media transformation workloads.
• Implement resilient workflows with retries, failure detection, fallback mechanisms, and graceful degradation when external AI providers experience delays or outages.
• Develop automated evaluation frameworks for dubbed audio naturalness, speech synchronization, translation accuracy, video clip selection, and brand compliance.
• Establish production quality gates using verification checks, silence detection, failure detection, evaluation scores, and targeted human review.
• Define service-level objectives (SLOs), observability requirements, incident response practices, and data-handling standards for enterprise production systems.
• Integrate voice-cloning consent, likeness permissions, content rights, and AI disclosure requirements into pipeline architecture and processing workflows.
• Set engineering standards for system design documentation, code reviews, testing, and production readiness.
• Provide technical mentorship through design reviews, code reviews, and hands-on engineering collaboration.
• Write critical production code and take high-priority features from early prototypes through implementation, testing, and deployment.
• Assess architectural bottlenecks and develop plans to support significant increases in processing volume, including scalability at 10 times current workloads.
• Improve automated quality coverage across output formats and track evaluation performance across successive releases.
• Reduce the time required to integrate new AI models, languages, and media output formats while maintaining enterprise-grade reliability.

Qualifications

Required Experience

• Typically 10+ years of professional software engineering experience, including substantial responsibility for designing, building, and operating production systems.
• Demonstrated technical ownership of software systems serving real users, with responsibility for availability, uptime commitments, operational reliability, and cost constraints.
• Hands-on experience deploying generative AI or multimodal AI systems into production environments.
• Practical experience delivering LLM applications, agentic workflows, multi-step AI processing pipelines, or retrieval-augmented generation (RAG) systems.
• Experience defining technical architecture and engineering direction for systems developed and maintained by other engineers.
• Demonstrated ability to take experimental AI capabilities from prototypes to reliable production implementations.

Required Skills

• Deep proficiency in Python and/or TypeScript for building and maintaining production software and AI-powered applications.
• Strong experience with at least one major cloud platform, including the ability to evaluate architectural approaches and explain technical tradeoffs.
• Practical knowledge of generative AI and multimodal model integration, including model orchestration, evaluation, reliability, and production deployment.
• Ability to design and operate multi-step AI workflows with robust evaluation mechanisms, retries, failure handling, and cost controls.
• Strong understanding of distributed systems, including job orchestration, message queues, storage infrastructure, and databases supporting large, long-running workloads.
• Ability to engineer reliable processing systems for workloads involving complex dependencies, extended execution times, and potential service failures.
• Experience establishing technical direction through architecture decisions, design documentation, engineering reviews, and mentoring.
• Ability to evaluate new AI models, experiment with emerging tools and agents, and determine their suitability for production use.
• Strong technical judgment when making architectural decisions under uncertainty and balancing system quality, execution speed, and commercial requirements.
• Ability to work independently in a remote environment, prioritize engineering outcomes, communicate technical decisions, and collaborate across engineering and creative disciplines.

Preferred Qualifications

• Experience with media processing technologies, including FFmpeg, audio and video codecs, media containers, transcoding, and loudness normalization.
• Familiarity with captioning and subtitle formats used in audio and video processing workflows.
• Experience with speech and audio machine learning, including automatic speech recognition, text-to-speech, voice cloning, speaker diarization, or lip synchronization.
• Knowledge of video understanding techniques such as scene detection, highlight extraction, automated clip selection, or multimodal embeddings.
• Experience self-hosting open-source AI models on GPU infrastructure, including capacity planning, utilization, and inference cost management.
• Experience implementing retrieval systems and vector search across large content catalogs.
• Background in media technology, publishing, podcasting, creator platforms, or enterprise media solutions.