Machine Learning Engineer

In charge of leveraging data and interventions to build a robust and scalable end-to-end learning-based control system.

Responsibilities

• Perform large-scale distributed training of neural networks for autonomous navigation tasks.
  Tackle tasks like decision-making, multi-agent interactions and agent modelling, trajectory generation etc.
• Participate in the complete neural network life cycle: develop a new task or neural architecture, optimize it for inference on NN accelerator, deploy it to a development build, and validate its safety with QA.

Criteria

• Strong experience with Python and software engineering best practices
• Experience with Pytorch or another major deep-learning framework
• An understanding of the under-the-hood knowledge of deep learning: layer details, loss functions, optimisation, etc.
• Expertise in deploying production ML models.
• Experience with Imitation Learning, Reinforcement Learning (offline/off-policy), modern Neural Network architectures (e.g., transformer, Diffusion), or related techniques.
• (Good to have) Background in designing, building, and producing motion planning algorithms, state estimation algorithms, and probabilistic modelling.