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Machine Learning Engineer - Multimodal
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$168,439 / year median in California
Job Description
- Multimodal Hadrian Automation Torrance, CA Job Details Full-time $160,000
- $250,000 a year 12 hours ago Benefits Health insurance Dental insurance 401(k) Vision insurance Life insurance Qualifications AI models PyTorch Software deployment AI platforms (beyond public GPTs) Machine intelligence Model deployment Developing large-scale AI models Model training System deployment Machine learning (ML) fundamentals Full Job Description Hadrian
- Manufacturing the Future Hadrian is building autonomous factories that help aerospace and defense companies manufacture rockets, satellites, jets, and ships up to 10x faster and up to 2x cheaper.
Our team owns problems end-to-end:
we design the software, define the manufacturing processes, and ensure they can be executed reliably in our factories. The work spans computational geometry, CAD/CAM integrations, high-performance systems, and full-stack web tooling. We execute whatever is required to deliver a working solution and best serve our users. The DFM team within Copilot is building the manufacturing data intelligence layer that serves as the tip of the spear for our automation stack. This platform ingests, interprets, and reasons over the full spectrum of manufacturing data (mechanical drawings, quality documentation, CAD data) and transforms it into structured, actionable information for the factory. As a Senior Machine Learning Engineer, you will own the ML lifecycle for the vision-language models that unify our perception and reasoning capabilities, building systems that understanding manufacturing drawings and design intent. What You'll Do Research, develop, and deploy multimodal models that understand content from manufacturing documentation end-to-end, replacing traditional OCR-to-NLP cascades with unified architectures Work alongside the core-engineering team to build and maintain annotation tooling, implement active learning loops, and engineer synthetic data augmentation strategies Develop evaluation frameworks that quantify system behavior and user impact, extending beyond traditional benchmark metrics Collaborate with the other members of the machine learning team to set the technical and product roadmaps for the AI platform Burn down the long tail, as every percentage point of accuracy maps to man-years of time savings at our scale What We're Looking For 5-8 years of professional deep learning experience, with at least 2 years working on multimodal models leveraging image and text modalities Strong Python and PyTorch fluency: You've written custom training loops, loss functions, and data loaders from scratch when needed Production deployment ownership: You've shipped models to production and have been responsible for endpoint and model health MS or PhD in Computer Science, Electrical Engineering, or related field preferred; equivalent industry experience valued equally Bonus Points You have a passion for manufacturing and believe that the industry needs better software Previously worked in aerospace, defense, or manufacturing, and have experience working with manufacturing data Published research, achieved SOA results on relevant benchmarks, or contribute to open-source frameworks Prior experience working in a high-ownership startup environment Compensation For this role, the target salary range is $160,000- $250,000 (actual range may vary based on experience).