Autonomous Driving ML Engineer - Real-Time Deployment

gatikaiinc; Santa Clara (CA); On-site; USD 170,000 - 240,000; Full time

Job summary

gatikaiinc is seeking a high-impact Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle stack. You will work cross-functionally with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient and production-ready for both on-vehicle and cloud workflows.

This onsite role at our Santa Clara, CA office requires deep expertise in ML workflows, real-time inference, and scalable model

Qualifications

• Data strategy, preprocessing, training, evaluation, deployment, and monitoring across ML lifecycle.
• Experience deploying neural networks for real-time or constrained systems is preferred.
• Strong foundations in ML workflows: curation, training, evaluation, and inference optimization.

Responsibilities

• End-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.
• Autonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.
• Efficient Neural Network Design: Optimize models using quantization, pruning, sparsification, compression, and efficient architectures.
• Real-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware.
• Model Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.
• Simulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.
• Scalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.
• Data Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.
• Cross-Functional Integration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.

Skills

Python

PyTorch

TensorFlow

C++

ML deployment

CUDA

Education

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

Tools

CUDA

TensorRT

Azure

Job description

gatikaiinc is seeking a high-impact Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle stack. You will work cross-functionally with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient and production-ready for both on-vehicle and cloud workflows.

This onsite role at our Santa Clara, CA office requires deep expertise in ML workflows, real-time inference, and scalable model