Machine Learning & AI · AI Engineering

Engai; New York (NY); Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Full healthcare coverage

Mental wellness stipend

Generous hardware allowance (M3 Max)

Job summary

Engai is seeking a Machine Learning Engineer — Multimodal & Vision to advance autonomous driving datasets by deploying advanced labeling models and zero-shot architectures. You will optimize end-to-end inference in production and work with global teams on scalable ML solutions.

You will fine-tune models such as SAM, YOLO, Mask R-CNN, and 3D detectors, and integrate model-assisted annotation APIs into customer MLOps infra while benefiting from flexible work hours and generous hardware support.

Qualifications

• 3+ years building and deploying deep learning models in production.
• Experience with computer vision or multimodal architectures.
• Strong Python and cloud experience (AWS/GCP).

Responsibilities

• Fine-tune and deploy assisted pre-labeling vision models (SAM, YOLO, Mask R-CNN, 3D point cloud detectors).
• Optimize model inference latency on GPU clusters using NVIDIA TensorRT, ONNX, and Triton.
• Evaluate model prediction confidence against human consensus to tighten the active learning loop.
• Integrate model-assisted annotation APIs into customer MLOps infrastructure.

Skills

Computer Vision & ML

Cloud systems

Real-time inference optimization

Tools

PyTorch

Docker

CUDA

ONNX

NVIDIA TensorRT

Triton

YOLO

Mask R-CNN

Job description

Machine Learning Engineer — Multimodal & Vision

Deploy assisted pre-labeling vision models and zero-shot architectures that dramatically accelerate throughput for autonomous driving and robotics datasets.

Responsibilities

• Fine-tune and deploy assisted pre-labeling vision models (SAM, YOLO, Mask R-CNN, 3D point cloud detectors)
• Optimize model inference latency on GPU clusters using NVIDIA TensorRT, ONNX, and Triton
• Evaluate model prediction confidence against human consensus to tighten the active learning loop
• Integrate model-assisted annotation APIs directly into customer MLOps infrastructure

Requirements

• 3+ years building and deploying deep learning models in production (PyTorch, Docker, CUDA)
• Proven expertise in Computer Vision, 3D LiDAR/sensor fusion, or multimodal vision-language architectures
• Strong software engineering fundamentals in Python and cloud systems (AWS/GCP)
• Experience optimizing deep learning inference for real-time interactive applications
• Dedicated cloud GPU compute budget for experimental research
• Full healthcare coverage including mental wellness stipends
• Flexible working hours across any global timezone
• Generous hardware allowance (M3 Max MacBook Pro or high-end Linux workstation)

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