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.

Role Description We are seeking to hire research engineers for robot dexterous manipulation with the goal of developing dexterous manipulation systems at human-level performance. Our strategy focuses on a real2sim2real pipeline that learns from large scale human videos and leverages simulation and reinforcement learning to bridge the human-to-robot embodiment gap. - Learning dexterous manipulation: Design and implement robot learning algorithms for manipulation - Leverage vision models to extract 3D hand-object manipulation information from real-world situations - Develop large-scale real2sim2real pipeline for dexterous manipulation - Multimodal Sensor Fusion: Integrate tactile sensing, proprioception and vision into the learning pipeline Qualifications - 5+ years of non-internship professional software development experience - 5+ years of programming with at least one software programming language experience - 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience as a mentor, tech lead or leading an engineering team - Bachelor's degree in computer science or equivalent Requirements - 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Experience with robotic manipulation - Experience/knowledge developing RL for manipulation - Experience with extracting human manipulation from large scale video and/or mocap data Benefits - Comprehensive health insurance (medical, dental, vision, prescription) - Basic Life & AD&D insurance and option for Supplemental life plans - EAP, Mental Health Support, Medical Advice Line - Flexible Spending Accounts - Adoption and Surrogacy Reimbursement coverage - 401(k) matching - Paid time off - Parental leave

• Design and deploy ML components for channel ranking and guide personalization • Work with linear scheduling data to create features for program relevance • Use Qdrant to group similar channels • Train and iterate on models using TensorFlow/PyTorch • Own delivery of defined tasks from data exploration to production deployment

• Lead the Ad Pod: Drive the technical roadmap for all Pluto-specific ML use cases, acting as the bridge between AMLG's core infrastructure and Pluto’s product goals. • Personalized Channel Discovery: Architect retrieval and ranking systems to recommend the right linear channels to users in real-time. • Dynamic Scheduling & Creation: Develop models that optimize channel scheduling and inform the creation of "Pop-up" or algorithmic channels based on viewer trends. • Multi-Objective Optimization: Design loss functions and reward systems that weigh user commitment (watch time) against business health (ad impressions and revenue). • EPG Personalization: Lead the vision for personalizing the Electronic Programming Guide, ensuring the most relevant content is surfaced immediately upon app launch. • Cross-Functional Leadership: Partner with RevOps and Product to translate business constraints (e.g., ad delivery guarantees) into technical ML requirements.