About the Role: This role targets excelling talent needed for key product areas, requiring significant domain expertise to accelerate critical initiatives . You will be expected to materially improve the quality and controllability of Adobe's generative multimodal models . This involves strengthening Adobe's competitive position in generative AI quality and alignment through sustained improvements in mid-training capabilities .
- Design and implement end-to-end training pipelines to build and improve editing quality for both images and videos.
- Lead core development for specific mid-training areas (e.g., image to image and image to video editing), while aligning with broader team strategy.
- Identify quality gaps in generative models and propose targeted mid-training solutions.
- Develop scalable workflows for data curation, data quality improvements, and distributed training.
- Increase engineering velocity and reducing iteration cost by systematizing mid-training experimentation and deployment.
- Partner closely with research, data, evaluation, infrastructure, pre-training and post-training teams to push the editing quality for both images and videos.
- Mentor junior engineers and contribute to raising the technical bar of the mid-training team.
- Master's degree or Ph.D. in Computer Science, Machine Learning, or a related field preferred.
- Proven track record in mid-training or continual pre-training of large-scale multimodal models, specifically on cross modality for image and video data.
- Deep understanding of pre-training, mid-training and/or post training for multimodal generative models.
- Deep understanding of modern diffusion-based architectures (DiT) and conditional generation and editing, e.g., instruct editing, to drive precise structural and temporal control in production environments.
- Strong expertise in Vision-Language Models (VLMs), including experience with contrastive learning, multimodal alignment, and leveraging VLM-based encoders to improve semantic understanding in generative tasks.
- Ability to design and implement scalable pipelines for data curation, data quality control, and distributed training in collaboration with data and infrastructure teams.
- Experience optimizing model inference and deployment for high-throughput product environments, ensuring a balance between generative quality and computational efficiency.
Expected Pay Range: Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $164,000 -- $313,300 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience.
Your recruiter can share more about the specific salary range for the job location during the hiring process.
In California, the pay range for this position is $216,400 - $313,300 In Washington, the pay range for this position is $204,800 - $296,600