Summary
What you’ll impact
Our company is seeking a Machine Learning Engineer to own end-to-end model quality for its autonomous robot fleet, handling dataset curation, training, evaluation, and production deployment. The role involves working with multimodal sensor data, developing perception, planning, navigation and manipulation models, and collaborating with teleoperations and fleet operations teams.
Responsibilities
What you'll do
- Own model quality end-to-end: dataset curation, training, and evaluation.
- Work with multimodal sensor data from Cameras, IMUs, LiDar and Odometry to shape the training set as the fleet grows.
- Train and evaluate policies, experiment with data, feature and architecture ablations
- Deploy your best results to production and build the systems that guarantee low latency, 24/7 inference on a fleet of robots.
- Work with Teleoperations and Fleet Operations to get the data the models need.
Requirements
What you’ll bring
- Trained large scale multimodal models on a fleet of GPUs using techniques such as video semantic segmentation, 6d pose estimation, VLAs and BEVs.
- Have owned a dataset end-to-end: what to collect, what to label, and what to cut.
- Write production training and inference code with Python, PyTorch or JAX, Triton.
- Have found and fixed a problem with a training run or a model's real-world performance, and can name the evidence that pointed to it.
- Experience working on Cluster management systems including Ray, Slurm or Kubernetes