Autonomous Driving ML Engineer: Real-Time Production

Gatik AI; Santa Clara (CA); Sur place; USD 170 000 - 240 000; Plein temps

Résumé du poste

Gatik AI seeks a high-impact Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle stack. You will collaborate with perception, prediction, planning, infrastructure, systems, and hardware teams to deliver scalable, real-time ML across on-vehicle and cloud workflows.

The role is onsite at our Santa Clara, CA office, requiring deep ML expertise and ownership of end-to-end model development, including data strategy, training, and deployment

Qualifications

• Deep ML lifecycle experience from data prep to deployment.
• Experience in end-to-end ML development for AV or robotics.
• Strong background in model optimization and real-time inference.

Responsabilités

• Own the full ML lifecycle from data strategy to deployment.
• Develop models for perception, prediction, planning, and scene understanding.
• Optimize models to meet latency, compute, memory, and power constraints.
• Integrate trained models into C++-based autonomous systems.
• Profile and optimize neural networks using CUDA and TensorRT.
• Collaborate with cross-functional teams to productionize ML components.

Connaissances

End-to-End model development

Autonomous driving models

Efficient neural network design

Real-time deployment

Model optimization

Simulation & evaluation

Scalable ML infrastructure

Data workflows

Cross-functional integration

Formation

MS/PhD in CS/ML/Robotics/EE/Statistics

Outils

CUDA

TensorRT

PyTorch

TensorFlow

C++

Python

Description du poste

Gatik AI seeks a high-impact Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle stack. You will collaborate with perception, prediction, planning, infrastructure, systems, and hardware teams to deliver scalable, real-time ML across on-vehicle and cloud workflows.

The role is onsite at our Santa Clara, CA office, requiring deep ML expertise and ownership of end-to-end model development, including data strategy, training, and deployment