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# Staff Machine Learning Engineer

- **Company:** Fruition Group
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Distributed Computing Environment, Machine Learning, Inference Optimization, Tensorflow, Model Compression, Reinforcement Learning, Pytorch, Evaluation Pipelines, Facebook Flow, Deep Learning, Discretization, Information Technology, ONNX (Open Neural Network Exchange) Format, TensorRT, Mixed Precision, Stable Diffusion
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_63cff79d6317bc39b3a28007c8687fb7

## About the Role

* A master's degree in computer science, electrical engineering, mathematics, statistics, or a related field; a PhD is a strong advantage.
 * 5+ years building and training deep learning models in PyTorch, TensorFlow, or JAX.
 * A record of taking models from training and evaluation through to production use.
 * A deep grasp of the current landscape: multimodal models, transformers, diffusion, and flow matching.
 * Experience with large-scale or distributed training.
 * Model compression and inference optimization (quantization, ONNX/TensorRT, mixed precision, custom kernels), post-training of large multimodal models, work with camera, lidar, and radar data, publications at leading ML and robotics conferences, or a background in autonomous driving or driver assistance.

## Description

Fruition Group is exclusively partnering with a fast-growing autonomous vehicle company to find a Staff Machine Learning Research Engineer. Our client is building large multimodal models that look at what the vehicle sees and work out what it should do next, and they want strong researchers who can take those models from experiments to running on real vehicles., * Build, train, and test vision-language-action models that propose high-level driving choices and guidance for the vehicle's path.

 * Take responsibility for a major research thread, covering the data, the model design, large training runs, and checking results on actual vehicles.
 * Create training and evaluation pipelines, along with demanding measures of how well models perform in driving situations.
 * Work out ways to shrink large models so they can run on the vehicle's own computer without losing what makes them useful.
 * Use supervised fine-tuning and reinforcement learning after pre-training to improve reasoning, reliability, and handling of unusual situations.
 * Work closely with the perception, planning, and platform teams to carry successful research into the production system.