Machine Learning Engineer — Multimodal & Vision

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

Role overview

Build and deploy machine learning systems that speed up the preparation of vision datasets for autonomous driving and robotics. The work spans model fine-tuning, efficient GPU inference, confidence evaluation against human labels, and integration with customer machine learning operations systems.

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.

Requirements

• At least 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.

Benefits and work setup

• Remote role based in the United States, with flexible hours across time zones.
• Salary range of $140,000–$185,000 plus equity.
• Cloud GPU budget for experimentation, healthcare coverage with mental wellness support, and a hardware allowance.