Koidra is hiring top-notch machine learning and computational engineers for highly specialized research directions that fuse Physics and Machine Learning with global application impact. Topics that we're dealing with on a regular basis include Graph Neural Network, Neural Differential Equations or Physics-informed Deep Learning, Model-based Reinforcement Learning, etc.
About Koidra
👉 Basic: AIoT startup, based in Seattle, founded by an ex Microsoft researcher
👉 Mission: modernize industrial controls and manufacturing with AIoT
We combine years of world-leading machine learning research, of industrial automation, and of manufacturing management to make products that break through previous barriers of automation
👉 Success: received global awards for its game-changing tech
Responsibilities
1️⃣ You will push the state of our proprietary physics-informed ML framework(s) to the next level
2️⃣ You will develop and train end-to-end models
3️⃣ You will develop AI for Good → You won’t target ads, optimize signup funnels, or recommend anything for anyone to click on.
Qualifications
Must Have
✅ Excellent programming, debugging, and analytical skills.
Our team loves writing clean, bug-free, and maintainable mathematical code. We take teamwork to the extreme and hence even for research projects, we still place a high bar for code clarity and robustness.
✅ Experience in constructing and training sophisticated Deep Learning models
✅ Solid understanding of mathematical modeling, differential equations, numerical methods, matrix computations
✅ Embrace https://koidra.io/culture
Preferred Qualifications
✅ Background in computational mathematics/physics/chemistry, scientific computing, control theory, or a related area
✅ Experience with Deep Learning fundamentals. We value candidates who
- are able to construct new models (as opposed to using stock CV/NLP models);
- understand the hierarchies and computational flow through each building block.
✅ Experience in building models for time series or sequential data (e.g. in NLP)
✅ Familiarity with and strong interests in one or more of the following machine learning research areas: Physics-informed Deep Learning, Graph Neural Network, Model-based Reinforcement Learning, Uncertainty Quantification