Principal AI Engineer, Media Pipelines

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, including ownership of production systems with real users, uptime commitments, and cost constraints. Production Generative AI Expertise: Demonstrated experience delivering LLM-powered or multimodal AI systems to production, including agentic workflows, multi-step pipelines, or retrieval-augmented generation (RAG). Production Readiness and Evaluation: Experience addressing the challenges that go beyond prototypes, including evaluation frameworks, retries, failure handling, monitoring, and inference-cost management. Programming and Cloud Infrastructure: Deep proficiency in Python and/or TypeScript, along with experience using at least one major cloud platform. Ability to select technologies based on technical and business requirements and clearly explain the trade-offs. Distributed Systems: Strong understanding of job orchestration, message queues, storage systems, databases, and the design of large-scale workloads that run for extended periods and must recover from failures. Technical Leadership: Experience setting architectural direction for systems used by other engineers, supported by design documentation, code reviews, and mentorship. AI-Native Mindset: Curiosity about emerging models and tools, with a habit of experimenting, building prototypes, and developing custom tools or agents. Ability to distinguish promising experiments from production-ready solutions. Sound Technical Judgment: Confidence making decisions with incomplete information, assessing risks, explaining assumptions, and changing direction when evidence warrants it. Collaborative Leadership: Strong communication skills, humility, integrity, and respect for both technical and creative disciplines. Ability to give and receive feedback constructively. Product and Business Orientation: A strong focus on execution, commercial value, customer needs, and measurable outcomes rather than experimentation for its own sake. Autonomy and Accountability: Ability to work effectively in a remote environment, prioritize independently against company goals, own outcomes, and keep stakeholders informed. Additional experience that would be advantageous includes: Media Processing: Familiarity with FFmpeg, codecs, containers, transcoding, loudness normalization, and caption or subtitle formats. Speech and Audio AI: Experience with speech recognition, text-to-speech, voice cloning, speaker diarization, or lip-sync technologies. Video Understanding: Knowledge of scene detection, highlight extraction, clip selection, or multimodal embeddings. GPU-Based Model Hosting: Experience self-hosting open-source AI models on GPUs, including capacity planning and cost optimization. Content Retrieval: Familiarity with retrieval systems and vector search across large content catalogs. Industry Experience: Background in media, publishing, podcasting, creator platforms, or providing technology solutions to enterprise media clients. Benefits: 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.