Description review

Steg.AI | Machine Learning Engineer | Irvine, CA (Onsite) | Full Time

Steg.AI · Irvine, CA (Onsite) · back to the listing

HR standards

66/100

needs work

Title ↔ description

83/100

solid

Reads as

Machine Learning Engineer

100% confident

What this role officially is

data scientist — ESCO, the EU occupation classification

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist

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What the listing never says

  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
  • No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity

The listing, marked up

Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.

Steg.AI develops AI-powered watermarking technology to protect and authenticate digital media. Our invisible watermarks are imperceptible to humans but robustly detectable by our proprietary models. Founded in 2019, we're an NSF- and investor-backed startup in Orange County, CA, with a team of 6 PhDs in computer vision.
We're hiring a Machine Learning Engineer to help push the frontier of AI for watermarking, steganography, and media provenance. You'll take state-of-the-art steganography models and own their journey to production — optimizing and deploying them on desktop and cloud platforms, serving as the link between research and customer-facing products.
Responsibilities: productionize novel steganography models, build inference pipelines, integrate models into cloud platforms, benchmark performance, and turn research into maintainable production code.
Required: PyTorch, ONNX export, experience deploying deep learning models to the cloud, Python and C/C++, strong cross-team communication.
Bonus: background in steganography/watermarking/media provenance, video/image codecs, MLOps tooling, CV/ML publications or open-source contributions.
To apply, send your resume to [email protected]

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 8 points (from 74 before penalties). Reviewed 21 Sep 2026.