akamor

Tonic AI (https://tonic.ai) builds the data infrastructure behind modern AI. We generate the synthetic environments that agents are trained and tested in, and we de-identify real enterprise data so it can be used safely in training and evaluation. Eight years in, we work with frontier AI labs pushing the edge of what models can do, and with hundreds of enterprises including Fidelity, Comcast, eBay, and Vanguard, on the data problems that sit at the center of where AI is going next.

We are looking for a data scientist with 8+ years experience (or 3+ with relevant PHD). In this role you will:

++ Design and build the systems that generate longitudinally coherent synthetic environments for agent training and evaluation, including persona modeling, task generators, and verifiable ground truth.

++ Build and maintain synthesis models that generate realistic replacement values at very large scale, preserving format, statistical distribution, and semantic consistency so de-identified data stays useful downstream.

++ Train and improve the NER models behind our entity detection, driving accuracy and recall across free text, structured fields, and mixed enterprise data at scale.

++ Build evaluation infrastructure that grades agent outcomes, not just traces, and produces real discrimination between frontier models on real tasks.

++ Fine-tune and evaluate open-weight models on Tonic-generated data, and turn benchmark results into product and research direction.

++ Expand coverage into new domains, languages, and entity types, and handle the long tail of formats and edge cases that real customer data throws off.

++ Own model evaluation across the board: precision and recall on detection, utility preservation on synthesis, and outcome-level grading for agents.

++ Optimize inference so models run efficiently on large volumes of sensitive data inside customer environments.

++ Partner directly with frontier labs and enterprise ML team to turn hard data problems into shipped model improvements.

++ Set technical direction for a small, senior team and raise the bar on rigor, reproducibility, and shipping.

Apply here: https://jobs.ashbyhq.com/TonicAI/048a114d-fb5f-46ef-b0ff-b62... but also shoot me an email at adam + (company domain name).

Does this work well on kvps and tables? That is where I typically have the most trouble with tesseract and where the cloud provider ocr systems really shine.

tl;dr NER models work best when they process data similar in structure to their training data. This notebook shows a few examples of how one can use an NER model not trained on JSON to actually identify and redact both structured and unstructured values with in a JSON blog. The NER model used is free available but closed source, but one can expect similar results with open source NER models such as spacy.

Is there a video somewhere of this in action?

There are also way too many people with access to non-anonymized data. i.e. the development team that has read privileges on the production database. e.g. employees at uber spying on customers (https://www.theguardian.com/technology/2016/dec/13/uber-empl...).

edit: shameless plug. check out tonic.ai for a solution to the above problem.

I'm one of the creators of Masquerade. It's open sourced and you can check it out here:

https://github.com/TonicAI/masquerade

Tonic AI | Atlanta or SF| Software Engineer | ONSITE

At Tonic we are building tools to help people create synthetic data that looks, feels, and acts like their real data, without compromising security, privacy, or regulatory compliance.

Looking for a full stack engineer with a preference for someone stronger with back-end technologies.

Tech Stack: dotnet core, java, react, python

Learn more: https://tonic.ai

E-mail us at hello AT tonic . ai

Paying market rate salaries + equity

Thanks. Our landing page is currently in a constant state of flux. I'll make sure that gets fixed. I'm a bit surprised but my previous reply actually generated a non insignificant amount of traffic to our site so we also opened up app.tonic.ai for anyone that wants to give the product a whirl.

Hi nagarjun. My company (https://tonic.ai) builds dev tools and we've noticed recently that companies with remote teams have been using our product for an unexpected use case. We are trying to investigate it further. Would you be willing to chat with me? If so, I'll drop you a way to get in touch and we can chat.

quick fyi, it does not compete with OP's product.

Thats awesome. Thanks a lot, arikfr.

Eddie, I think it would be neat if we could build vendor images by just supplying docker containers with maybe some type of config.

At Tonic (https://tonic.ai) we do on-prem deploys with docker containers and docker-compose. Its seamless and would be great to use that same flow for Digital Ocean marketplace.

Hey folks, I helped build Tonic Document Masker. If you have any questions let us know and we'll be happy to answer.

Hi, this is Adam. I'm a founder at Tonic.

As others have said, we've found a lot of smaller companies will test with production data because of their need/desire to move quickly. But we've also seen much much larger companies use production data in their dev/staging environments. Sometimes there will be production-like safeguards and security measures in place but not always. People shy away from practices that slow down development and testing.

We think synthetic data is the right solution for a few reasons. Most importantly, we believe it provides the right level security, while still allowing your team to be productive, i.e., your business logic and test cases still work. It also allows you to scale really easily since you effectively have a ruleset for generating data of any size. Finally, it’s a great way to share data throughout your organization and can help facilitate sales and partnerships. If you’re curious about scaling, check this post out: https://www.tonic.ai/blog/condenser-a-database-subsetting-to...

Hey folks. Adam here. I work at Tonic and helped developed Condenser.

Happy to answer any questions and in the meantime, if you'd like to learn more you can also read more on our blog.

https://www.tonic.ai/blog/condenser-a-database-subsetting-to...