Remote ML Vision Engineer for Autonomous Driving

Engai; United States; Remote; USD 140,000 - 185,000; Full time

Benefits offered by this job

Remote role (US)

Equity

Healthcare coverage

Hardware allowance

Flexible hours

Job summary

Engai is hiring a role to build and deploy machine learning systems that speed up the preparation of vision datasets for autonomous driving and robotics. This position focuses on model fine-tuning, efficient GPU inference, confidence evaluation against human labels, and seamless integration with customer MLOps systems.

The candidate will fine-tune labeling models, accelerate inference with TensorRT/ONNX/Triton, and collaborate on active learning workflows while aligning with customer ML

Qualifications

• Three+ years building and deploying production deep learning models.
• Experience with PyTorch, Docker, and CUDA.
• Expertise in computer vision, 3D LiDAR or sensor fusion, or multimodal vision-language systems.
• Strong Python and cloud engineering skills, including AWS or GCP.
• Experience optimizing inference for real-time interactive applications.

Responsibilities

• Fine-tune and deploy assisted labeling models, including segmentation, object detection, and 3D point cloud approaches.
• Improve GPU inference speed using tools such as TensorRT, ONNX, and Triton.
• Compare model confidence with human consensus to improve active learning and review workflows.
• Integrate model-assisted annotation APIs into customer MLOps infrastructure.

Skills

Python

Deep learning

Computer vision

3D LiDAR

Multimodal vision-language

Tools

PyTorch

Docker

CUDA

AWS

GCP

Job description

Engai is hiring a role to build and deploy machine learning systems that speed up the preparation of vision datasets for autonomous driving and robotics. This position focuses on model fine-tuning, efficient GPU inference, confidence evaluation against human labels, and seamless integration with customer MLOps systems.

The candidate will fine-tune labeling models, accelerate inference with TensorRT/ONNX/Triton, and collaborate on active learning workflows while aligning with customer ML