Comment by happycry
4 days ago
Resemble AI | San Francisco Bay Area (Mountain View, CA) | Full-Time
We're creating state of the art AI models, including the leading foundation model for Deepfake Detection. Looking for full stack, frontend and machine learning engineers that would love to create prototypes directly with the founders.
Here's what we're looking for:
Full Stack Engineer - We are seeking a skilled Full Stack Engineer with experience with both Javascript and Python to join us. The ideal candidate will have a strong background in building and deploying web applications, a passion for working on cutting-edge AI technologies, and the ability to collaborate closely with our founders to create innovative prototypes.
Product Lead - Ownership over our security AI model product suite. Lead technical + product conversations with customer engineering/security stakeholders.
Solutions Engineer / Forward Deployed Engineer - Own technical success for enterprise integrations: take customers from first POC → production rollout → expansion. Partner with product/engineering to turn customer friction into roadmap, and advocate for the highest-leverage fixes.
If interested, reach out directly to me: zohaib [at] resemble.ai
Are you tunnel visioning on detecting outputs of a technology using the same fundamental technology? It seems like that would be an inescapable product-risk that I would bet against.
If you discover the best AI detection technology, then it will eventually be used as a (negative) reward function in training future models. It's an arms race.
Would you be open to alternative techniques, like metadata, source analysis, or online (fuzzy) reverse searching? Consider that judges and court experts don't rely solely on the evidence itself to determine if it's 'real', but to an even greater extent they rely on how it was collected, if evidence has not been sourced correctly, judges don't even look at it.
Detection is one layer, not the whole solution. We've also built an AI model that inserts invisible watermarks for content provenance.
We've also been doing active work in source tracing: https://github.com/piotrkawa/audio-deepfake-source-tracing
I think there's a lot of precedent for invisible watermarks for source identification in both copyright protection and exfiltration identification. Of course this is quite possible without compute intensive ML models.
It's a job posting, not an AMA by the CTO
Questions are pretty standard in hiring. At least to determine if there's a good fit.