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Tier-1 brand, metro location, and mid-level ML generalist role increase qualified applicant competition.
Requires specialized GenAI, GPU, and MLOps expertise, limiting cross-industry transferability.
Explicit 6+ years, 4+ years leading GPU GenAI systems, and mandatory GPU/ML stack make filters strict.
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Own design and development of scalable, high-performance generative AI services and APIs integrating multiple generative models into Adobe flagship products.
Lead optimization of GPU-accelerated pipelines for inference and training focusing on latency, throughput, scalability, and reliability.
Set technical direction and mentor machine learning engineers in implementing production-scale GenAI systems and workflows.
6+ years of machine learning experience including production-scale deployments.
4+ years leading large-scale, GPU-intensive generative AI systems covering training, inference, and optimization.
Proficiency with GenAI frameworks/tools such as PyTorch, CUDA, Triton, TensorRT, Nvidia Dynamo, and Python.
Good understanding of generative model architectures including diffusion models, transformers, and GANs.
Experienced in building and optimizing ML workflows for enterprise-scale model customization and serving in production environments.
Familiarity with large-scale model serving, inference orchestration, and GPU resource management, preferably with Kubernetes and distributed systems.
Capable of collaborating cross-functionally with research and product teams to define inference strategies and productize generative AI models at scale.