Make the Dot is transforming fashion design with a platform that combines a digital canvas, fabric library, and AI tools to streamline workflows. Designers at brands like DKNY and Gap use our tools to cut design time from months to days, reducing costs and physical samples. Since 2023, we’ve achieved 12x ARR growth and adoption by top design schools like Parsons and FIT.
We’re hiring a machine learning engineer to refine custom models, improve our ML stack, and develop solutions for designing and visualizing complex garments.
What You’ll Work On
Fine-tuning Diffusion (transformer) models for image generation
Design, deploy, and maintain Diffusion models for cloud-based inference
Transform research models into production-ready demos and MVPs
Optimize model inference for improved performance and scalability
Ensure high availability and reliability of model serving infrastructure
Ensure security best practices across the ML infrastructure
Develop and maintain robust APIs for serving machine learning models
What We’re Looking For
Strong proficiency in Python and its ecosystem for machine learning, transformer models, data analysis, and other NN architectures.
Fine-tuning Diffusion models for image generation, image upscaling, in and out painting models, etc.
Deep understanding of how to effectively evaluate image generative models
Strong proficiency in PyTorch, transformer models
Knowledge of cloud platforms (AWS, GCP, or Azure) for deploying and scaling ML services
Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes)
Proven track record in rapid ML model prototyping using tools like Streamlit or Gradio
Experience with distributed task queues and scalable model serving architectures
Understanding of monitoring, logging, and observability best practices for ML systems
Nice to haves
Experience with frontend development frameworks (e.g., Vue.js, Angular, React)
Knowledge of database systems and data streaming technologies
Understanding of security best practices for API development and ML model serving
Experience with real-time inference systems and low-latency optimizations
Announce date: January 26, 2025
Application deadline: Feb 26, 2025
Expire date: March 26, 2025
Open to public (Anyone can apply)
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