Machine Learning Engineer

Machine Learning Engineer

Department
Research
Location
HCMCSeattleRemote
Keywords
machine learningphysicsgraph neural networkreinforcement learningdifferential equationsscientific simulation

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

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

  1. are able to construct new models (as opposed to using stock CV/NLP models);
  2. 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: Graph Neural Network, Model-based Reinforcement Learning, Uncertainty Quantification