Senior research Scientist - Machine Learning Systems & Efficiency Engineer

Photoshop ART is seeking a Senior researcher - Machine Learning 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 href="https://www.adobe.com/adobe-for-all.html#:~:text=and%20culture.-,Our%20company%20values.,-As%20we%20look">values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create.

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  Expected Pay Range:

  Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $142,700 -- $270,950 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process. In California, the pay range for this position is $187,100 - $270,950 In Washington, the pay range for this position is $168,600 - $244,200

  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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  Colorado:

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  If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.

  Massachusetts:

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