akamor

Joined 9 years ago

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12
Submissions
21
Member since
Oct 1
developer, head of engineering @ tonic (https://tonic.ai)
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Recent submissions

Comment · 3 months ago
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).

Comment · 2 years ago
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.
Comment · 2 years ago
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.
Comment · 7 years ago
Is there a video somewhere of this in action?
Comment · 7 years ago
Comment · 7 years ago
Comment · 7 years ago
Comment · 7 years ago
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.
Comment · 7 years ago
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.

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