Principal AI Engineer, Media Pipelines

Inception Point AI

. Drive the product roadmap and lead execution on the core content transformation pipeline .

Posted 10/14/2026full-timeRemote • United StatesLead💰 $175,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in building and managing production generative AI systems, with a strong focus on technical direction, architecture decisions, and automated quality assurance. Proficient in Python and/or TypeScript, with a solid understanding of cloud platforms and job orchestration for large-scale workloads.

Highest-signal resume keywords

Production Generative AI ExperiencePython and/or TypeScript ProficiencyTechnical Direction and Architecture DecisionsJob Orchestration and Database ManagementMentoring and Code Review

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Production Software DevelopmentGenerative AI SystemsJob OrchestrationData ModelsQuality ControlAutomated Quality GatesModel EvaluationsCost ManagementAPI ManagementCloud Platform Experience

Soft Skills

Independent WorkRemote CollaborationMentoring

Tools & Technologies

FFmpegSpeech RecognitionText-to-SpeechVoice CloningGPU ManagementTranscodingDatabasesQueues

Industry Keywords

MediaPublishingPodcastingCreator PlatformsEnterprise Media Clients

Tech Stack

Tools & technologies

CloudFFmpegPythonTypeScript

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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