Research Engineer

Waabi Waabi · Robotics · US & Canada, Dallas, TX +4 · Remote · Autonomy & Algorithms

Research Engineer role focused on advancing and deploying AI algorithms for self-driving vehicles, bridging research insights with production systems and ensuring high-quality code. The role involves working with large datasets and simulations, collaborating with multidisciplinary teams on perception, forecasting, planning, and simulation, and contributing to publications. Experience in applied research and turning research ideas into practical solutions is required.

What you'd actually do

  1. Prototype, evaluate, and iterate on solutions, using real-world data and simulations, to improve the accuracy, robustness, and safety of our self-driving algorithms.
  2. Formulate problems and propose pragmatic and long-term solutions based on research insights, leveraging your expertise in deep learning, computer vision, and self-driving technologies.
  3. Support deploying solutions to our production systems, collaborating closely with platform teams to ensure seamless integration of research findings into our self-driving system.
  4. Champion engineering excellence, ensuring high-quality, well structured and tested code.
  5. Collaborate in a multidisciplinary team solving problems related to perception, motion forecasting, planning, traffic modeling and sensor simulation, integrating research findings into our self-driving system and contributing to the development of a unified self-driving platform.

Skills

Required

  • Python programming
  • computing fundamentals
  • code efficiency
  • large datasets for machine learning applications
  • applied research projects

Nice to have

  • Master/PhD in machine learning, computer science, engineering, or a related field
  • shipping machine learning features/models into production
  • machine learning literature
  • self-driving technology
  • publications in top-tier conferences or journals
  • working with platform teams
  • concurrent, parallel, and distributed computing techniques
  • Pytorch
  • Rust
  • C++
  • CUDA

What the JD emphasized

  • turning them into practical solutions for real-world applications
  • shipping machine learning features/models into production

Other signals

  • advancing and deploying artificial intelligence algorithms for self-driving vehicles
  • develop cutting-edge solutions that enable our vehicles to understand and navigate the world
  • bring research ideas into production
  • prototype, evaluate, and iterate on solutions
  • deploying solutions to our production systems
  • integrating research findings into our self-driving system