Principal AI Engineer, Media Pipelines

Job not on LinkedIn

🔥 13 hours ago

🇺🇸 United States – Remote

💵 $175k - $200k / year

⏰ Full Time

🔴 Lead

🤖 AI Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Inception Point AI

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2023

📱 Media

🤖 Artificial Intelligence

👥 B2C

Media • Artificial Intelligence • B2C

Inception Point AI is a company focused on creating AI personalities for media through technology and storytelling. They envision a future where AI beings are prevalent in digital content creation, bringing these artificial beings to life and establishing a next-generation content business model that allows them to own both talent and content, all driven by artificial intelligence. Their expertise spans across media production, video content, podcasting, and social media platforms such as YouTube and TikTok.

📋 Description

• Drive the product roadmap and lead execution on the core content transformation pipeline • Set technical direction for media ingestion, transformation, quality control, and delivery • Make architecture decisions regarding orchestration, data models, and service boundaries, and document them • Decide what to build, buy, or fine-tune across LLMs, speech recognition, text-to-speech, translation, and video models • Run structured model bake-offs and design provider-swappable pipelines • Manage processing cost, GPU and API spend, latency, throughput, batching, caching, routing, and graceful degradation • Build evaluations and automated quality gates for dub naturalness and sync, translation accuracy, clip selection, brand compliance, verification, silence detection, and failure detection • Define SLOs, observability, incident response, and data handling for client catalogs • Address voice-cloning consent, likeness, content rights, and AI disclosure requirements • Set engineering standards for design documents, code review, and testing • Mentor engineers through pairing and review • Write critical production code and deliver high-priority roadmap features from prototype to production • Partner with the Lead AI Engineer and cross-functional leads on technical direction and architectural standards • In the first 90 days, learn the pipeline end to end, ship a high-priority feature, write an architecture review, and run a model bake-off • In the first 12 months, reduce cost per processed minute, establish automated quality gates and tracked evaluation scores, accelerate addition of models/languages/output formats, and improve enterprise reliability

🎯 Requirements

• Typically 10+ years building software, including time as the technical owner of a production system with real users, uptime commitments, and cost constraints • Production generative AI experience, including shipped LLM or multimodal systems such as agentic workflows, multi-step pipelines, or RAG • Experience handling evaluations, retries, failure modes, and cost in production AI systems • Deep experience in Python and/or TypeScript • Experience with at least one major cloud platform • Strong grounding in job orchestration, queues, storage, and databases for large, long-running, failure-prone workloads • Experience setting technical direction through design docs, reviews, and mentoring • Ability to work well remotely and operate independently • Nice to have: FFmpeg, codecs and containers, transcoding, loudness normalization, caption or subtitle formats • Nice to have: speech recognition, text-to-speech, voice cloning, speaker diarization, or lip-sync • Nice to have: scene detection, highlight and clip selection, or multimodal embeddings • Nice to have: self-hosting open-source models on GPUs, including capacity and cost planning • Nice to have: retrieval and vector search over large content catalogs • Nice to have: experience in media, publishing, podcasting, creator platforms, or selling to enterprise media clients • Must answer whether sponsorship to work in the United States is required

🏖️ Benefits

• Equity