Deep Learning Engineer

tuul.ai; Illinois; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote-friendly

Europe/US time zones

Job summary

tuul.ai is seeking a Deep Learning Engineer to design, train, and deploy computer vision and spatial reasoning models. You will work across the full pipeline from data preparation to edge deployment on mobile devices, with remote-friendly collaboration.

The role emphasizes hands-on experience with PyTorch, CNN/Transformer architectures, and model optimization, plus strong Python skills and the ability to iterate rapidly.

Qualifications

• Experience with PyTorch in production (2+ years).
• Strong understanding of CNN and Transformer architectures.
• Hands-on model training from scratch and fine-tuning.
• Experience with model optimization and deployment (quantization, pruning, ONNX).
• Experience with computer vision tasks (detection, segmentation, tracking).
• Strong Python skills and ML tooling familiarity.
• Ability to debug model behavior and iterate quickly.

Responsibilities

• Design, train, and deploy deep learning models for CV and spatial tasks.
• Work across the full pipeline from data preparation to production deployment on mobile/edge devices.
• Collaborate with team and clients to deliver impactful features.

Skills

PyTorch production experience

CNN/Transformer architectures

Training from scratch & fine-tuning

Model optimization & deployment

Computer vision tasks

Python & ML tooling

Debugging & rapid iteration

Job description

Deep Learning Engineer

Full-time

• Remote-friendly
• Europe/US time zones

What You'll Work On

You'll be designing, training, and deploying deep learning models for computer vision and spatial reasoning tasks. This includes everything from object detection and segmentation to novel 3D understanding problems. You'll work across the full pipeline - from data preparation to production deployment on mobile and edge devices.

What We're Looking For

• Strong experience with PyTorch (2+ years production experience)
• Deep understanding of modern CNN and Transformer architectures
• Hands-on experience training models from scratch and fine-tuning
• Knowledge of model optimization and deployment (quantization, pruning, ONNX)
• Experience with computer vision tasks (detection, segmentation, tracking)
• Strong Python skills and familiarity with ML tooling
• Ability to debug model behavior and iterate quickly

Nice to Have

• Experience with 3D deep learning (point clouds, voxels, implicit representations)
• Knowledge of spatial AI and geometric deep learning
• Background in multi-view geometry and 3D reconstruction
• Familiarity with mobile ML frameworks (CoreML, TensorFlow Lite)
• Experience with self-supervised or weakly-supervised learning
• Knowledge of synthetic data generation and domain adaptation

How We Work

• Small team, high impact
• Real problems, not toy datasets
• Ownership over features and projects
• Direct client interaction and feedback
• Flexible, remote-friendly

What We're Working On

See our portfolio to understand the types of projects we tackle. Current focus areas include mobile AI applications, offline-first systems, and agricultural technology.