Computer Vision Engineer

Simuletic; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote role

GPU access

Customer feedback

Job summary

Simuletic seeks a skilled computer vision engineer to build and improve data-generation and model-evaluation workflows for security, defense and robotics applications. You will oversee the full cycle—from generating and annotating examples to training models, measuring performance and refining pipelines—in a small team with direct input into what ships.

You will create ComfyUI workflows, fine-tune LoRA models on domain data, train and benchmark YOLO detectors and vision-language or

Qualifications

• Strong Python and PyTorch skills.
• Experience training, fine-tuning and deploying YOLO models, version 8 or later.
• Familiarity with vision transformers and vision-language models.
• Ability to build and modify ComfyUI workflows.
• Experience fine-tuning LoRAs with SDXL, Flux or comparable models.
• Working knowledge of YOLO and COCO annotation formats.

Responsibilities

• Create and maintain ComfyUI workflows for generating training data across relevant scenarios.
• Fine-tune LoRA models on domain data such as equipment, vehicles and environments.
• Train and benchmark YOLO detectors and vision-language or vision transformer models using synthetic and real data.
• Compare model performance on generated and real-world data, then use findings to improve generation quality and dataset coverage.
• Build fine-tuning workflows for vision-language tasks such as captioning, grounding and classification.
• Maintain annotation accuracy and dataset quality as work is released.

Skills

Python

PyTorch

Vision transformers

Computer vision

Tools

ComfyUI

YOLO (v8+)

CUDA

ONNX/TensorRT

Job description

Role overview

Build and improve the computer vision data-generation and model-evaluation workflows used for security, defense and robotics applications. You’ll take work through the full cycle—from generating and annotating examples to training models, measuring performance and refining the pipeline—in a small team with direct input into what ships.

Responsibilities

• Create and maintain ComfyUI workflows for generating training data across relevant scenarios.
• Fine-tune LoRA models on domain data such as equipment, vehicles and environments.
• Train and benchmark YOLO detectors and vision-language or vision transformer models using synthetic and real data.
• Compare model performance on generated and real-world data, then use findings to improve generation quality and dataset coverage.
• Build fine-tuning workflows for vision-language tasks such as captioning, grounding and classification.
• Maintain annotation accuracy and dataset quality as work is released.

Requirements

• Strong Python and PyTorch skills.
• Experience training, fine-tuning and deploying YOLO models, version 8 or later.
• Familiarity with vision transformers and vision-language models.
• Ability to build and modify ComfyUI workflows.
• Experience fine-tuning LoRAs with SDXL, Flux or comparable models.
• Working knowledge of YOLO and COCO annotation formats.

Nice to have

• Knowledge of diffusion model components or tools such as ControlNet.
• Experience in CCTV, defense, robotics or synthetic data.
• Deployment experience with CUDA, Triton, ONNX or TensorRT.
• Open-source contributions and comfort delivering work in a small team.

Benefits and work setup

• Remote role, with EU-friendly working hours. The position offers GPU access for training and experimentation, ownership of technical direction, and direct feedback from customers.