Dear applicants, please keep in mind that applications without provided salary expectations and active LN profile will not be considered.
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
- 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)
- 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