Role Description Photoshop ART is seeking a Senior Machine Learning (ML) Systems & Efficiency Engineer to join our R&D team focused on delivering practical, production-ready improvements in inference performance, latency, and cost efficiency across image editing applications. This role sits at the intersection of model architecture, systems, inference runtimes, and services, with a clear mandate: deliver high-quality ML systems at substantially lower cost and higher efficiency. Individuals in this role are expected to have deep expertise in areas such as Artificial Intelligence (AI), ML systems, and computer vision. Strong preference will be given to candidates with experience in distributed inference, multimodal model profiling, and performance optimization. You will work closely with research, product, and infrastructure teams to influence model design decisions, improve GPU utilization, and build scalable, cost-aware ML systems deployed in production. This is a hands-on, high-leverage role where a single engineer can drive outsized impact, potentially saving millions of dollars in compute costs. The ideal candidate will have a strong interest in developing practical innovations that advance Adobe products. Qualifications - Master’s or PhD in Computer Science, Electrical Engineering, or a related field, with a focus on machine learning systems, distributed systems, or high-performance computing. - Hands-on experience implementing and scaling large-scale inference or serving workloads using distributed frameworks and runtime systems (e.g., Triton, vLLM, SGLang, xDiT, or similar). - Experience applying inference compilation and optimization tools (e.g., TensorRT, ONNX Runtime, AOTI), including techniques such as operator fusion and graph-level optimization. - Strong understanding of GPU architecture (e.g., memory hierarchy, compute throughput, communication bandwidth) and practical experience diagnosing performance bottlenecks across compute, memory, and I/O subsystems. - Proficiency in Python and C++, with experience building high-performance or distributed systems. - Familiarity with CUDA or Triton for performance-critical workloads is highly desirable. - Demonstrated ability to make engineering decisions based on rigorous measurement and benchmarking, with a focus on improving system efficiency, scalability, and reliability in production environments. Requirements - 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. - Write and maintain high-performance GPU kernels using Triton or CUDA to accelerate custom model layers and critical workloads. - Conduct deep performance analysis using tools such as PyTorch Profiler and NVIDIA Nsight to identify bottlenecks in compute, memory, and communication. - Partner with infrastructure teams to design scalable and reliable distributed serving systems across heterogeneous hardware environments. - Establish and track efficiency metrics such as cost per million inferences. - Serve as a trusted technical advisor to research and product teams on efficiency tradeoffs. Benefits - Competitive salary and performance-based incentives. - Comprehensive benefits programs. - Opportunities for professional development and growth. Company Description Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity. Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact.