Machine Learning Engineer
Steg.AI · Artificial Intelligence
unknown
Salary Range (USD)
Negotiable
Location
Irvine, USA
Visa Support
Not mentioned
Funding Stage
startup
Job Responsibilities
- • productionize novel steganography models
- • build inference pipelines
- • integrate models into cloud platforms
- • benchmark performance
- • turn research into maintainable production code
Required Skills
PyTorchONNX exportexperience deploying deep learning models to the cloudPythonC/C++strong cross-team communication
Engineering Culture & Tech Stack
PyTorchONNXPythonC/C++
ownership
cross-team collaboration
product-minded
Raw Post
Show original text
Steg.AI | Machine Learning Engineer | Irvine, CA (Onsite) | Full Time
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 careers@steg.ai
AI Risk Insights
No major risk signals detected.
Recent News
No recent updates
Data Source
Content parsed by LLM from Hacker News raw data. Confidence:HIGH