Palo Alto, CA /
At Matician, our goal is to save people time and energy by automating mundane tasks inside the home. We believe that sensors and algorithms are finally good enough that we can apply Level 5 Autonomy and mobility in order to reimagine home devices. We are building great products to solve real problems and ship them to the people we love. Our mission-driven and tight-knit group values learning and curiosity in a high-risk, high-reward culture.
We're looking for motivated computer vision & machine learning engineers to join us (pre-launch) on the ground floor, with runway for huge and immediate impact.
What you'll do
- Design, build and code new neural network architectures, loss functions and enforce novel constraints
- Train, test and tune NN models, gather and refine data
- Deploy model at scale with high run-time efficiency
- Analyze results, continuously update and improve accuracy & speed of various ML/CV algorithms
- Be an integral member of our small perception software team
- Work to enable Level 5 autonomy on consumer robotics
- Collaborate with Hardware, Software, and Algorithms team to bring product vision to life
What we look for
- Ability to write clean, reliable code that is easily maintained
- Fluency with Python, C, C++ (PyTorch or Numpy a plus)
- Experience working on CV & ML problems
- Comfort with fast-paced, startup atmosphere
- Risk taking, a propensity for learning, and no fear of failure
We'd love to hear from you if...
- You are genuinely motivated to help those around you
- You are passionate about learning outside of your normal comfort zones
- You love diagnosing complex technical issues
- You are excited to do great work
If you got to this point, we hope you're feeling excited about this opportunity at Matician! Even if you don't feel that you meet every single requirement, we still encourage you to apply. We're eager to meet people who are keen to learn and are passionate about what Matician is building. We want to hear how you can contribute to our team in a variety of ways – not just the above boxes.