Applied Research Engineer - Video Understanding

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Hope for your understanding.

Location: San Francisco, CA

Employment Type: Full-Time ONSITE

Visa Sponsorship: H-1B, O-1, OPT supported

We are an AI research lab focused exclusively on video data. Video represents the dominant digital medium globally — powering creativity, communication, gaming, AR/VR, robotics, and beyond. The biggest bottleneck in advancing these systems is high-quality training data at scale.

Our team combines:

• Exabyte-scale video infrastructure
• Novel video understanding techniques
• Large-scale multimodal datasets
  
  
  

We partner with leading AI labs and recently completed a Series A round backed by Tier 1 investors. The team is lean (≈12 people), high-signal, and operating at the frontier of multimodal AI.

As an Applied Research Engineer, you will build high-performance pipelines and infrastructure to understand video with precision at internet scale.

This role sits between research and production:

• Not purely academic research
• Not pure infrastructure engineering
• You will work on ambiguous, open-ended problems in:
• Computer Vision
• Audio Processing
• Multimodal (video + text + audio) systems
  
  
  

You’ll design clever techniques to extract signal from large-scale data while optimizing performance and cost.

What You’ll Do

• Build scalable pipelines for video understanding
• Work with large models and APIs, optimizing inference performance
• Apply pre- and post-processing techniques to improve model precision
• Implement parallelization, pipelining, and inference optimization strategies

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• Occasionally fine-tune models where needed
• Break down customer-level requirements into technical building blocks
• Write clean, production-ready Python code
• Collaborate with customers and external research teams
• Contribute to the evolution of next-generation video datasets
  
  
  

Requirements

• 5+ years experience in computer vision or audio processing
• Strong Python skills
• Hands-on experience with PyTorch (or similar ML frameworks)
• Experience working with large models or model APIs
• Ability to optimize inference pipelines
• Clear communication skills (technical + external stakeholders)

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• Strong ownership mindset
• In-person presence in San Francisco
• Experience building large-scale multimodal systems
• Startup experience (early hire)
• Open-source contributions
• Published research (bonus, not required)
• Demonstrated performance optimization work
• Passion for video / media technologies
  
  
  

Interview Process

• Initial Screen
• Technical Discussion with CTO
• Deep Technical Interview
• Conversation with CEO
• On-site
• Offer