Machine Learning / AI Engineer
HOMEKYND · United States
Retail Furniture and Home Furnishings · 2-10 employees
About the role
Design and deploy ML models for object detection, scene understanding, and 3D asset placement within a spatial intelligence platform. Collaborate with cross-functional teams to integrate AI features and optimize model performance for real-time inference.
What they look for
Requirements
Requires 3+ years of ML development experience focusing on computer vision and proficiency in Python and major ML frameworks. Candidates should have expertise in generative models, 3D data processing, and efficient model deployment.
Full description
Machine Learning / AI Engineer
Homekynd is building the spatial intelligence layer for enterprise retail. Our platform transforms
photos into 3D room models and powers immersive furniture visualization at scale, deployed in
physical retail stores and embedded across enterprise ecommerce. We're a remote-first team
on a fast build timeline, and we need engineers who want real ownership over hard problems.
As Machine Learning / AI Engineer, you'll develop and integrate AI-driven features directly into
the 3D visualization platform. Object placement, scene analysis, asset generation - you're
applying machine learning to problems most engineers never get close to.
What you'll do
- Design, develop, and deploy ML models for object detection, scene understanding, and
3D asset placement
- Train and fine-tune models on 3D datasets to generate realistic visualizations while
preserving image fidelity
- Collaborate with graphics and full-stack teams to integrate AI models into the product
pipeline
- Implement tools to automate image segmentation, furniture recognition, and style
recommendations
- Optimize model performance for real-time or near-real-time inference at scale
- Stay current on generative AI and ML advancements and bring relevant capabilities into
the platform
What we're looking for
- 3+ years in ML development with a focus on computer vision
- Proficiency in Python and ML frameworks including PyTorch, TensorFlow, or Keras
- Strong understanding of generative models (Stable Diffusion, GANs, VAEs) and LLM-
based integrations
- Experience with 3D data processing including point clouds, mesh recognition, and
geometry analysis
- Familiarity with object detection and segmentation tools (YOLO, Mask R-CNN)
- Ability to deploy models efficiently using TensorRT, ONNX, or serverless cloud
deployments
Bonus
- Experience with ControlNet for AI-driven image conditioning
- Familiarity with 3D file formats and workflows (glTF, OBJ)
- Experience with cloud-based ML platforms such as Vertex AI, AWS SageMaker, or
Hugging Face
- Background in recommendation systems for design suggestions or automated staging
