About the Role
The role involves designing, training, and deploying machine learning models with a focus on diffusion techniques and visual data systems, contributing to both product features and core algorithmic improvements.
Responsibilities
- Design and implement diffusion-based generative models
- Optimize vision models for inference speed and accuracy
- Collaborate on end-to-end ML pipeline development
- Conduct experiments to validate model performance
- Improve training efficiency and resource utilization
- Integrate models into production systems
- Debug and resolve model performance issues
- Work closely with research teams to implement new methods
- Maintain documentation for model architectures and workflows
- Evaluate ethical implications of model outputs
- Support data curation and labeling pipelines
- Monitor model behavior in live environments
- Contribute to version control and reproducibility practices
- Participate in code reviews and technical design discussions
- Develop tools for model evaluation and diagnostics
Nice to Have
- Advanced degree in computer science or related discipline
- Publications or contributions in ML or vision conferences
- Experience with latent diffusion models
- Knowledge of attention mechanisms and transformers
- Prior work with generative adversarial networks
- Familiarity with MLOps tooling
- Contributions to open-source machine learning projects
- Experience in high-performance computing environments
Compensation
Competitive salary and equity package
Work Arrangement
Hybrid work model with flexible remote options
Team
Small, cross-functional team focused on rapid experimentation and deployment
Tech Stack
Python, PyTorch, Hugging Face, CUDA, Docker, Kubernetes, Git, Weights & Biases
Application Process
- Submit resume and GitHub profile
- Complete a technical screening
- Participate in a modeling challenge
- Engage in team interviews
Available for qualified candidates
