Senior Machine Learning Engineer, Applied - United States or Canada
About the role:
We are looking for Machine Learning engineers passionate about generative models and the creative applications of AI to join our Applied ML team. We look for highly innovative ML engineers with a strong blend of research and engineering skills who are motivated to push the boundaries of image, video, and 3D foundation models and their applications. In this role, you will collaborate with research and platform teams to build, improve, and optimize workflows that power state-of-the-art application solutions based on diffusion models. You will have access to high-performance computing resources and work alongside top researchers and engineers, making a real impact in the rapidly growing field of generative AI.
Responsibilities:
- Lead efforts to drive the design, develop, prototype and productionize ML systems that address the customer needs
- Collaborate with the research team on developing the next generation of models, where you may assist with areas such as optimization of model training, model fine-tuning, dataset engineering, tooling, open-source efforts, etc.
- Work on the commercial side, productionizing generative model-based solutions, by partnering with platform and research teams building the infrastructure to serve them at scale
- Write production-quality code and influence the next generation of Stability AI’s machine learning solutions
- Advocate for engineering best practices and upskill the team
Qualifications:
- MS or PhD in Computer Science or related technical discipline
- +3 years working on machine learning projects
- Experience working with diffusion models
- Proficiency with Python scientific stack, PyTorch, creating Jupyter/Colab notebooks
- Strong coding skill and comfortable with coding in production platforms
- Ability to diagnose technical problems, debug code, and automate manual routine tasks
- Ability to communicate machine learning concepts and results effectively through writing and visualization
- Track record of publication in top-tier machine learning conferences
