Machine Learning Software Engineer

Applied Intuition Applied Intuition · Robotics · Stuttgart · SDS Software Engineering

Machine Learning Software Engineer for autonomous vehicles or mobile robots, focusing on perception, prediction, or planning. The role involves building ML capabilities, designing and implementing systems, and leveraging existing infrastructure. Requires experience in the end-to-end development cycle of deep learning models, production software development, and ML/DL perception algorithms for autonomous vehicles.

What you'd actually do

  1. Design and implement capabilities and workflows for cutting-edge real-world perception or planning/prediction systems
  2. Leverage established products at Applied Intuition to build the software and infrastructure foundation for our ML developments

Skills

Required

  • Experience with the end-to-end development cycle of deep learning models
  • Expertise in subdomains such as modeling, input pipelines, evaluation, deployment, and model optimization
  • 3+ years of experience building production software using modern software practices
  • Fluency in C++, or fluency in Python with intermediate experience in C++
  • Deep understanding of the concepts and methods behind any frameworks or libraries that they worked with
  • Experience working with production level ML and DL perception algorithms for autonomous vehicles

Nice to have

  • MSc or PhD in machine learning, ideally applied to perception, prediction,planning or closely related field
  • Experience building and shipping software frameworks or tools
  • Experience with driver assistance or autonomous driving systems
  • Experience in evaluating and improving system-in-the-loop model performance
  • Deep hands-on expertise in relevant algorithms or methods, such as non-linear optimization, computational geometry, numerical analysis, or distributed systems

What the JD emphasized

  • end-to-end development cycle of deep learning models
  • building production software using modern software practices
  • production level ML and DL perception algorithms for autonomous vehicles

Other signals

  • building out key ML capabilities of an autonomous vehicle stack
  • end-to-end development cycle of deep learning models
  • building production software using modern software practices
  • production level ML and DL perception algorithms for autonomous vehicles