Senior ML research Engineer

Machine Learning Systems Engineer The Opportunity: The Adobe Applied Research Team is looking for a Machine Learning Systems Engineer. You will take ownership of implementing low‑level optimizations that make our foundation models faster, leaner, and more scalable. This role is designed for a high‑potential engineer who is passionate about the intersection of hardware and AI. You will work directly on the infrastructure that powers Adobe’s next generation of Multimodal and Video models, focusing on implementing the next‑gen efficient model architecture and squeezing every TFLOP of performance out of our GPU clusters. Job Responsibilities Kernel Development & Optimization: Write and maintain high‑performance GPU kernels using Triton or CUDA to accelerate custom model layers and operations. Training Efficiency: Support the scaling of large‑scale training runs. You will help implement and debug distributed training strategies including ZeRO , Tensor Parallelism , and Pipeline Parallelism . Inference Acceleration: Implement state‑of‑the‑art inference techniques such as quantization (FP8/INT8) , speculative decoding , and KV cache optimizations to reduce latency and cost‑per‑token. Performance Profiling: Conduct deep‑dive bottleneck analysis using tools like PyTorch Profiler , NVIDIA Nsight , and Nsight Systems to identify stalls in compute, memory bandwidth, or NCCL communication. System Maintenance: Collaborate on the Model Factory pipeline to ensure training jobs are fault‑tolerant and utilize cluster resources efficiently across InfiniBand/RoCE networks. What You’ll Need to Succeed Education: Master’s or PhD’s degree in Computer Science, Electrical Engineering, or a related field with a focus on Parallel Computing or Systems. Foundational Systems Programming: Proficiency in Python and C++ , with a solid understanding of memory management and concurrency. Hands‑on ML Frameworks: Experience working with PyTorch or JAX, specifically moving beyond simple model training into distributed setups (e.g., PyTorch FSDP, DeepSpeed). GPU Awareness: A strong grasp of GPU architecture (SRAM vs. HBM, warps, and thread blocks) and how these impact the performance of ML workloads. Analytical Mindset: A “measure twice, cut once” approach to engineering—you enjoy looking at execution traces and flame graphs to find 10% wins. Preferred Experience Experience contributing to open‑source efficiency libraries (e.g., vLLM , FlashAttention , or TensorRT‑LLM ). Exposure to low‑level communication libraries like NCCL and an understanding of collective operations (AllReduce, AllGather). Familiarity with containerization ( Docker/Kubernetes ) and job scheduling in a headless Linux environment. Knowledge of modern model architectures like Mixture‑of‑Experts (MoE) or Diffusion Transformers (DiT). Expected Pay Range U.S. pay range: $172,500 – $306,625 per year. In California the range is $211,800 – $306,625; in Washington $201,000 – $291,150. Equal Employment Opportunity Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more. Accessibility Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email View email address on click.appcast.io or call View phone number on click.appcast.io. #J-18808-Ljbffr Frame.io