Principal AI Engineer, Media Pipelines

Jobgether; United States; On-site; USD 190,000 - 270,000; Full time

Benefits offered by this job

Technical ownership

Cross-functional collaboration

Professional growth

Competitive compensation

Health insurance

Job summary

Jobgether seeks a Principal AI Engineer, Media Pipelines in the United States to lead architecture and roadmaps for AI-powered media transformation. You will shape pipelines turning large catalogs into short‑form videos, multilingual dubs, and formats optimized for AI discovery, while balancing cost, quality, and reliability.

You will own the technical direction across multimodal AI, distributed systems, and cloud infrastructure, mentoring engineers and driving hands‑on delivery from prototype

Qualifications

• Extensive software engineering background with production systems.
• Experience delivering multimodal AI systems to production.
• Strong cloud and distributed systems expertise.

Responsibilities

• Define architecture for media pipelines and AI components.
• Lead evaluation of models and vendor technology choices.
• Drive cost, latency, and throughput optimizations at scale.
• Mentor engineers and set coding and testing standards.

Skills

Python

TypeScript

Cloud platforms

LLM

Multimodal AI

Distributed systems

Cost optimization

Mentorship

Architectural leadership

Product roadmap

Tools

AWS

GCP

Kubernetes

FFmpeg

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal AI Engineer, Media Pipelines based in the United States.

Take a leading technical role in building AI-powered media transformation systems that help creators and media brands extend the reach of their content across platforms and languages. As a Principal Engineer, you will define the architecture and technical roadmap for pipelines that transform large media catalogs into short-form videos, multilingual dubs, and formats optimized for AI-powered discovery. You will combine hands-on engineering with strategic decision-making across multimodal AI, distributed systems, and cloud infrastructure. A key focus will be improving processing speed, controlling inference costs, and ensuring consistently high-quality outputs at scale. Working closely with AI specialists and cross-functional leaders, you will establish engineering standards and guide critical features from experimentation to production. This is an opportunity to shape a rapidly evolving product while solving complex challenges at the intersection of AI, media, and enterprise reliability.

Accountabilities

• 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

• 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.

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.