Job Description
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Company Overview
A leading global visual discovery platform connects millions of people with creative ideas, inspiration, and possibilities for their lives. The organization combines technology, creativity, artificial intelligence, and machine learning to build products that help people discover and visualize new ideas.
Its AI and machine learning teams develop advanced technologies that enhance creativity and improve user experiences. The organization places strong emphasis on innovation, collaboration, responsible AI development, and enabling employees to do their best work with flexibility.
The organization is seeking a Sr. Machine Learning Engineer, Applied Science to join its Applied ML research and development organization. This role will focus on developing large-scale generative computer vision models and advancing visual modeling technologies that power visualization, inpainting, and outpainting experiences.
The successful candidate will work with rich visual-text datasets to develop foundation models that can be continuously deployed to production. The role offers the opportunity to collaborate closely with a small, highly specialized engineering team while contributing to multimodal representation learning, generative modeling, and other advanced machine learning initiatives.
Key Responsibilities
- Prototype new model architectures for an internal text-to-image generative model.
- Develop and implement large-scale diffusion-based text-to-image models.
- Conduct independent research and model implementation focused on generative computer vision.
- Read and analyze research papers and participate in technical discussions and brainstorming sessions.
- Contribute to the development of the organization’s overall visual generative modeling strategy.
- Help collect and curate relevant visual training data for generative modeling initiatives.
- Support reinforcement learning from human feedback (RLHF), targeted fine-tuning, and related model optimization activities.
- Collaborate with engineers, researchers, and product prototyping teams to advance visual modeling capabilities.
- Contribute to multimodal text and image embedding development and related machine learning systems.
- Publish and communicate research findings through conferences, academic papers, technical blog posts, and other appropriate channels.
- Mentor junior researchers and research interns within the applied machine learning organization.
- Apply AI-assisted development tools to improve experimentation, development, debugging, testing, documentation, and engineering workflows where appropriate.
Required Qualifications
- 5+ years of industry experience in computer vision.
- Hands-on experience working with generative computer vision models.
- Strong experience with diffusion models or related generative modeling techniques.
- Demonstrated ability to independently implement and experiment with machine learning models.
- Master’s degree or PhD in Machine Learning, Computer Science, or a related technical field.
- Strong understanding of machine learning and computer vision research methodologies.
- Ability to read, understand, and apply findings from machine learning research papers.
- Strong collaboration and communication skills.
- Ability to explain technical approaches clearly and demonstrate sound problem-solving processes.
Preferred Qualifications
- Publications at leading machine learning conferences.
- Experience developing large-scale text-to-image generative models.
- Experience with multimodal representation learning.
- Experience with reinforcement learning from human feedback (RLHF).
- Experience with targeted model fine-tuning and visual training data development.
- Experience using AI coding assistants such as Cursor, Copilot, Codex, or similar tools for development, debugging, testing, and refactoring.
- Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL and data exploration, and engineering workflow acceleration.
- Experience mentoring junior researchers, engineers, or research interns.
- Experience contributing to research publications, conference submissions, technical blogs, or similar public technical work.
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