AI Engineering Lead

OpusClip

$150K — $180K *
Consumer Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Proven experience shipping AI/ML models from prototype to production at scale.
  • Deep expertise in model post-training, including optimization and user feedback integration.
  • Strong inference optimization skills, focusing on performance metrics like latency and cost.
  • Robust model evaluation capabilities, covering both qualitative and quantitative assessments.
  • Hands-on experience with LLMs or multimodal models relevant to video applications.
  • Familiarity with video-related AI models and systems, including ASR and enhancement techniques.
  • Ability to guide strategic decisions between proprietary and open-source solutions.

Responsibilities

  • Lead the development and deployment of AI/ML models from conception to production.
  • Optimize model performance through fine-tuning and inference enhancements.
  • Establish and maintain robust model evaluation practices to ensure quality and effectiveness.
  • Design data flywheels that leverage user feedback for continuous model improvement.
  • Mentor and manage a small team of AI engineers, fostering a high-performance culture.
  • Set and communicate the technical roadmap and architectural best practices.
  • Make informed build-vs-buy decisions regarding AI tools and models.

Benefits

  • Opportunity to lead innovative AI projects in a cutting-edge environment.
  • Engagement in a product-oriented and consumer-focused team.
  • Collaborative workspace fostering creativity and experimentation in AI.
  • Chance to work with the latest technologies in video-related AI applications.
Full Job Description
We want to hire a hands-on AI Engineering Lead with strong technical depth and some people-management experience.

What we are looking for
  • Proven experience shipping AI/ML models from prototype to production at scale.
  • Deep expertise in model post-training, including fine-tuning, preference optimization, evaluation, and learning from user feedback. Experience building data flywheels that turn user behavior and feedback into training data and continuous model improvements.
  • Strong inference optimization experience for self-hosted models, including latency, throughput, GPU utilization, quantization, and serving cost.
  • Strong model evaluation skills, including offline benchmarks, human evaluation, online experiments, and product-quality metrics.
  • Practical experience applying LLMs or multimodal models to video applications, such as highlight detection, content curation, ranking, personalization, and editing decisions.
  • Familiarity with video-related models and systems, including but not limited to vision-language models, ASR/transcription, video understanding, and enhancement/upscaling, etc..
  • Able to make build-vs-buy decisions across proprietary APIs, open-source models, and internally trained models.
  • Capable of setting the technical roadmap, reviewing architecture, mentoring engineers, and managing a small high-performing AI team.
  • Product-oriented and pragmatic: understands how to balance model quality, latency, reliability, and infrastructure cost.

Nice to have
  • Experience with consumer video, creator tools, recommendation/content systems, or other high-scale multimodal products is plus.

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