Senior Staff Machine Learning Scientist, Assets

Responsibilities

• Lead and drive ambitious research initiatives that advance the state of the art in computer vision, multimodal understanding, and visual generation.
• Develop novel models, algorithms, and training methodologies for challenging vision problems such as image understanding, video understanding, visual search, scene representation, segmentation, detection, generation, and multimodal reasoning.
• Translate cutting-edge research into practical model improvements that can shape product direction and unlock new user experiences.
• Design, implement, train, and optimize large-scale vision and multimodal foundation models across diverse datasets and tasks.
• Partner closely with applied scientists, ML engineers, and product teams to move research from exploration to production-ready systems.
• Set technical direction for high-impact research areas, identifying promising bets and influencing longer-term strategy for vision and multimodal AI.
• Mentor other scientists and engineers, raise the quality bar for research, and help build a strong scientific culture across the team.
• Stay at the forefront of research in computer vision, multimodal learning, generative modeling, and foundation models, and apply emerging techniques to real-world problems.

Requirements

• BA/BS degree or equivalent experience.
• Deep theoretical and practical expertise in computer vision, with strong foundations in areas such as representation learning, visual recognition, image or video generation, multimodal learning, and large-scale model training.
• Advanced degree in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related field; PhD strongly preferred.
• 8+ years of relevant industry and/or research experience, or equivalent impact, including a track record of driving major research initiatives in computer vision or multimodal machine learning.
• Strong experience with state-of-the-art vision and multimodal model architectures, including transformers, diffusion models, contrastive learning approaches, and foundation models.
• Proven ability to formulate research problems clearly, design rigorous experiments, and translate findings into meaningful model or product advances.
• Strong coding and prototyping skills in Python, with the ability to write clean, scalable, and well-documented research code.
• Proficiency in modern deep learning frameworks such as PyTorch and TensorFlow.
• Demonstrated technical leadership, including mentoring scientists or engineers and influencing research direction across a team or organization.
• Strong communication skills, with the ability to present complex research clearly to both technical and cross-functional audiences.

Compensation

United States (USD):

• Zone A: 220,000 - 285,000
• Zone B: 207,000 - 268,000
• Zone C: 194,000 - 251,000