Senior research Scientist - Machine Learning Systems & Efficiency Engineer

Adobe is seeking a Senior Researcher - Machine Learning Systems & Efficiency Engineer to join their R&D team focused on improving inference performance and cost efficiency across image editing applications. The role involves optimizing ML systems, collaborating with various teams, and driving innovations that enhance Adobe products.

Responsibilities:

• Design and optimize high-throughput, low-latency inference systems
• Optimize model architectures to improve deployment and runtime efficiency using techniques such as distillation, pruning, quantization, and Mixture-of-Experts (MoE)
• Implement advanced serving strategies including batching, caching (KV, semantic, embedding), quantization (FP8/INT8), and distributed inference strategies including data, tensor, pipeline, expert, and hybrid parallelism, with a focus on balancing computation and communication efficiency
• Explore training or fine-tuning approaches when they directly lead to more efficient inference, simpler deployment, or improved runtime performance
• Write and maintain high-performance GPU kernels using Triton or CUDA to accelerate custom model layers and critical workloads
• Improve GPU utilization through kernel fusion, asynchronous pipelines, and optimized scheduling strategies
• Conduct deep performance analysis using tools such as PyTorch Profiler and NVIDIA Nsight to identify bottlenecks in compute, memory, and communication
• Optimize end-to-end system performance across inference workloads
• Partner with infrastructure teams to design scalable and reliable distributed serving systems across heterogeneous hardware environments (e.g., A100, H100, B200, CPU)
• Contribute to resource scheduling, GPU pooling, and elastic workload management
• Establish and track efficiency metrics such as cost per million inferences
• Build benchmarking frameworks and dashboards to guide tradeoffs among quality, latency, and compute cost, enabling data-driven system and product decisions
• Serve as a trusted technical advisor to research and product teams on efficiency tradeoffs
• Define best practices for scalable and cost-efficient ML development and mentor engineers on performance-oriented systems design