Principal AI Engineer, Media Pipelines

Liquid Studios AI

1d•$175,000 - $200,000

About The Position

Liquid Studios is seeking a hands-on, AI-native Principal Engineer to drive the product roadmap and lead execution of its core content transformation pipeline. The pipeline turns media catalogs into platform-native short-form clips, multilingual dubs, and AI-discoverable formats. This is a senior individual-contributor role, partnering closely with the Lead AI Engineer and cross-functional leads to set technical direction and architectural standards. The role owns architecture and technical direction across media ingestion, transformation, quality control, and delivery. It addresses the challenges of orchestrating video, speech, and text models across large catalogs while controlling inference cost and latency, and establishing evaluations that determine whether outputs are ready to ship. The Principal Engineer will set pipeline architecture and engineering standards; determine model and vendor strategy; manage cost, latency, and throughput; build evaluation systems and automated quality gates; define enterprise reliability practices; and incorporate rights, consent, and safety requirements. The role also involves mentoring engineers and personally writing critical code and delivering high-priority roadmap features from prototype to production. Success in the first 90 days includes learning the end-to-end pipeline, shipping a high-priority feature, documenting how the pipeline will scale to 10x volume, and conducting a structured model bake-off that changes a production model choice. Within 12 months, success includes reducing cost per processed minute, tracking automated quality evaluations for every output type, making it possible to add models, languages, or formats in days, and providing enterprise clients with clear SLOs, incident reporting, and reliable operation.

Requirements

Typically 10 or more years of software engineering experience, including technical ownership of a production system with real users, uptime commitments, and cost constraints.
Experience shipping generative AI systems, such as LLM or multimodal systems, agentic workflows, multi-step pipelines, or retrieval-augmented generation (RAG), to production.
Experience handling production AI concerns including evaluations, retries, failure modes, and cost.
Deep experience with Python and/or TypeScript and at least one major cloud platform; ability to explain tool and architecture tradeoffs.
Strong grounding in distributed systems and data, including job orchestration, queues, storage, and databases for large, long-running, failure-prone workloads.
Experience setting technical direction for systems used by other engineers through design documents, reviews, and mentoring.
Ability to work experimentally and make sound decisions with incomplete information, while determining when prototypes are ready for production.
Strong collaboration skills, receptiveness to feedback, humility, integrity, and respect for technical and creative disciplines.
Execution-focused, product-minded approach that prioritizes commercial utility and user impact.
Self-directed and proactive, able to work remotely, prioritize against company goals, and keep others informed without day-to-day oversight.

Nice To Haves

Experience with media processing, including FFmpeg, codecs and containers, transcoding, loudness normalization, and caption or subtitle formats.
Experience with speech and audio machine learning, including speech recognition, text-to-speech, voice cloning, speaker diarization, or lip-sync.
Experience with video understanding, including scene detection, highlight and clip selection, or multimodal embeddings.
Experience self-hosting open-source models on GPUs, including capacity and cost planning.
Experience with retrieval and vector search over large content catalogs.
Experience in media, publishing, podcasting, or creator platforms, or selling to enterprise media clients.

Responsibilities

Set technical direction for the pipeline covering media ingestion, transformation, quality control, and delivery; make and document decisions about orchestration, data models, and service boundaries.
Determine what to build, buy, or fine-tune across language, speech recognition, text-to-speech, translation, and video models; run structured evaluations of new models and design for replaceable providers.
Own unit economics per processed minute and manage GPU and API spending through batching, caching, and routing; ensure graceful degradation when providers are slow or unavailable.
Build evaluations for dub naturalness and synchronization, translation accuracy, clip selection, and brand compliance; implement automated quality gates and targeted human review.
Define SLOs, observability, incident response, and data-handling practices for client catalogs, including unreleased or rights-restricted content.
Work with leadership on voice-cloning consent, likeness, content rights, and AI disclosure, and incorporate those requirements into the pipeline.
Establish engineering standards for design documents, code review, and testing; mentor engineers through pairing and review.
Write a meaningful share of critical code and take high-priority roadmap features from prototype through production.
Learn the pipeline end to end, ship a high-priority roadmap feature, assess how the pipeline scales to 10x volume, and run a model bake-off during the first 90 days.
Reduce cost per processed minute, track automated quality gates and evaluation scores for all output types, speed up adding models, languages, and formats, and provide enterprise-grade reliability within the first 12 months.