AI Engineering Lead
About The Position
OpusClip is the world's No.1 AI video agent, built for authenticity on social media. We envision a world where everyone can authentically share their story through video, with no expertise needed. Within just 18 months of our launch, over 10 million creators and businesses have used OpusClip to enhance their social presence. We have raised $50 million in total funding and are fortunate to have some of the most supportive investors, including SoftBank Vision Fund, DCM Ventures, Millennium New Horizons, Fellows Fund, AI Grant, Jason Lemkin (SaaStr), Samsung Next, GTMfund, Alumni Ventures, and many more. We want to hire a hands-on AI Engineering Lead with strong technical depth and some people-management experience.
Requirements
- 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 Haves
- Experience with consumer video, creator tools, recommendation/content systems, or other high-scale multimodal products is plus.
Responsibilities
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
