Principal Data Scientist, Gen AI and Vision
New
Based in the United StatesFull-TimePrincipal
SalaryCompetitive base salary with performance-based annual bonus eligibility
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Job Details
- Experience
- 10+ years
- Required Skills
- PyTorchDeep LearningMLOpsGenerative AIComputer Vision
Requirements
- Master’s or PhD in Computer Science, Machine Learning, AI, or a related field.
- 10+ years of experience in deep learning, computer vision, generative AI, or multimodal model development.
- Deep expertise in generative modeling techniques such as diffusion models, world models, or advanced vision systems.
- Strong proficiency in PyTorch and modern ML workflows, including training, fine-tuning, evaluation, and inference optimization.
- Proven experience adapting large pre-trained models to complex, real-world production environments.
- Strong understanding of model evaluation, quality assessment, and tradeoff analysis between accuracy, performance, and scalability.
- Demonstrated technical leadership at a principal or staff level, with experience owning architecture or end-to-end systems.
- Experience working across research and engineering teams to deliver production-grade AI solutions.
Responsibilities
- Define and lead the technical strategy for generative AI and computer vision systems, including multimodal and simulation-grounded model architectures.
- Own the end-to-end model lifecycle, from research exploration and prototyping to production deployment and performance optimization.
- Design and improve model controllability, realism, temporal consistency, robustness, and scalability across visual AI systems.
- Establish evaluation frameworks, benchmarks, and release criteria in collaboration with evaluation and platform engineering teams.
- Drive fine-tuning, adaptation, and hybrid modeling strategies to meet domain-specific and production requirements.
- Identify and mitigate model failure modes such as drift, instability, and output inconsistency across complex systems.
- Guide architectural decisions, inference optimization strategies, and model deployment tradeoffs for production readiness.
- Mentor and provide technical leadership to data scientists and machine learning engineers within the team.
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