Senior Applied Scientist Machine Learning Systems Engineer- Photoshop

The Opportunity

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.

Job Responsibilities

• Inference & Serving Optimization: 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.
• Kernel Development & System Acceleration: 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.
• Performance Profiling & System Optimization: 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.
• Distributed Systems & Infrastructure Collaboration: 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.
• Cost-Aware ML Engineering: 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.
• Technical Leadership & Best Practices: 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.

What Youll Need to Succeed

• Education: Masters or PhD in Computer Science Electrical Engineering or a related field with a focus on machine learning systems distributed systems or high-performance computing.
• Distributed Inference & Serving Expertise: 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 with a strong understanding of system-level performance tradeoffs.
• GPU & Performance Engineering Skills: 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.
• Programming & Systems Development: 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.
• Data-Driven Engineering Mindset: 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.

Preferred Experience

• ML Frameworks & Tooling: Experience contributing to or maintaining performance- or efficiency-focused libraries or systems. Hands-on experience with:
  • Open-source serving frameworks (e.g. vLLM SGLang xDiT or similar)
  • Inference compilation tools (e.g. TensorRT Triton AOTI or equivalent operation fusion or graph-level optimization)
  • GPU profiling and performance analysis tools (e.g. PyTorch Profiler NVIDIA Nsight CUDA tooling)
    
• Distributed Systems & Communication: Exposure to low-level communication libraries such as NCCL and a practical understanding of collective operations (e.g. AllReduce AllGather) in large-scale distributed serving environments.
  
• Containerization & Cluster Operations: Familiarity with containerized workflows (Docker Kubernetes) and job scheduling in headless Linux environments including experience operating production ML workloads on shared GPU clusters.
  
• Model Architectures: Working knowledge of model architectures such as Transformers multimodal models Mixture-of-Experts (MoE) or Diffusion Transformers (DiT).

About Adobe

Adobe empowers everyone to create through innovative platforms and tools that unleash creativity productivity and personalized customer experiences. Adobes industry-leading offerings including Adobe Acrobat Studio Adobe Express Adobe Firefly Creative Cloud Adobe Experience Platform Adobe Experience Manager and GenStudio enable people and businesses to turn ideas into impact powered by AI and driven by human ingenuity.

Our 30000 employees worldwide are creating the future and raising the bar as we drive the next decade of growth. Were on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.

Lets Adobe together

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Expected Pay Range:

At Adobe for sales roles starting salaries are expressed as total target compensation (TTC base commission) and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition certain roles may be eligible for long-term incentives in the form of a new hire equity award.

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