Performance Architect Jobs

About the Role Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability. We're looking for a Staff ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly. This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech. Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech. Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference. Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure. Build quality evaluation frameworks that guide model selection for customers and inform the roadmap. Join a small, early-stage team with outsized impact on a fast-growing product area. Responsibilities Own the voice inference roadmap end-to-end — define and execute the technical strategy for optimizing STT, TTS, and speech-to-speech models across Together's infrastructure, with a clear-eyed view of where the field is heading and how to position the platform ahead of it. Drive best-in-class inference performance — architect and implement systems targeting leading

Memory Systems Performance & Workload Architect - SMTS — 2 Locations. Apply via Workday.

About the Job Lead performance work that turns large-scale AI workloads into faster, more efficient systems. As a Staff AI Performance Engineer, you will analyze and optimize AI training and inference workloads across distributed systems. Your work will connect compute, memory, networking and software behavior. You will lead complex performance workstreams across hardware and software. You will find bottlenecks, validate improvements and turn performance evidence into practical engineering decisions. You will build benchmarks, profile demanding workloads and develop C++ and Python tools for analysis and optimization. Your recommendations will help teams improve efficiency, scalability and reliability. This role gives you broad technical scope across AI infrastructure at data center scale. It is based in Milpitas, California. The team and culture The System Engineering Performance team architects, evaluates and optimizes high-performance infrastructure for large-scale data center deployments. The team works across the computing stack to understand real system behavior. Work moves through evidence, ownership and clear technical judgment. You will lead investigations, coordinate across teams and validate impact with reliable performance data. Decisions are shaped through benchmarks, models, simulations and practical engineering tradeoffs. The team values engineers who think big, act fast, take responsibility, speak up and lead beyond their own area. What we’re looking for · Strong experience profiling and optimizing AI, machine learning or high-performance computing workloads. · Experience with distributed systems and communication libraries such as MPI, NCCL, UCX or libfabric. · Strong C++ and Python skills, including reliable tools or performance-sensitive software. · Deep understanding of compute, memory and communication behavior in large-scale systems. · Ability to lead technically complex work and coordinate improvements across teams. · Familiarity with MLPerf, a

About the Job Set the performance direction behind large-scale AI systems built for real deployment. As a Principal AI Performance Engineer, you will define how Graphcore analyzes and optimizes AI training and inference workloads. Your work will shape decisions across hardware, software, networking and system architecture. You will lead the hardest performance investigations, from model behavior to multi-node scaling. You will turn profiling, benchmarking and modeling evidence into technical roadmaps and engineering priorities. You will set measurement standards, guide distributed communication strategy and review critical C++ and Python tools. Your judgment will help teams make better decisions when data is incomplete. This role gives you rare influence across AI infrastructure at data center scale. It is based in Austin, Texas. The team and culture The System Engineering Performance team architects, evaluates and optimizes high-performance infrastructure for large-scale data center deployments. The team works across the computing stack to understand real system behavior. Work happens through evidence, technical ownership and direct cross-team influence. You will guide strategy, resolve complex tradeoffs and raise performance-engineering standards across the organization. Decisions are shaped by benchmarks, simulations, models and clear technical debate. The team values engineers who think big, act fast, take responsibility, speak up and lead beyond their own area. What we’re looking for · Deep experience profiling and optimizing AI, machine learning or high-performance computing workloads at scale. · Proven technical leadership across complex performance-engineering or system-architecture initiatives. · Expert C++ and Python skills, including reliable tools or performance-sensitive software. · Expert understanding of compute, memory, communication and scaling behavior in distributed systems. · Ability to define benchmarks, measurement practices or optimization sta

About the Job Turn complex AI workload signals into faster, more efficient systems at data center scale. As a Senior AI Performance Engineer, you will analyze and optimize AI training and inference workloads across large-scale distributed systems. Your work will connect compute, memory, communication and software behavior. You will own complex performance investigations from problem definition through validation. You will turn profiling, benchmarking and modeling results into improvements engineers can build from. You will design benchmarks, investigate system bottlenecks and optimize distributed communication software across technologies such as MPI, NCCL, UCX and RDMA. Your work will help Graphcore deliver efficient, reliable AI systems for large-scale deployment. This role gives you rare scope across hardware, software, networking and system architecture. It is based in Austin, Texas. The team and culture The System Engineering Performance team architects, evaluates and optimizes high-performance infrastructure for large-scale data center deployments. The team works across the computing stack to understand real system behavior. Work moves through evidence, ownership and clear technical judgment. You will define investigations, coordinate across teams and validate impact with reliable performance data. Decisions are shaped through benchmarks, models, simulations and practical engineering tradeoffs. The team values engineers who think big, act fast, take responsibility, speak up and lead beyond their own area. What we’re looking for · Strong experience profiling and optimizing AI, machine learning or high-performance computing workloads. · Experience with distributed systems and communication libraries such as MPI, NCCL, UCX or libfabric. · Strong C++ and Python skills, including reliable tools or performance-sensitive software. · Deep understanding of compute, memory and communication behavior in large-scale systems. · Ability to own complex technical work and coo

About Graphcore Graphcore is a leading innovator in artificial intelligence computing. We develop hardware, software, and data center infrastructure that provide the specialized processing and systems capabilities needed to advance AI while improving the efficiency required for broad adoption. As part of SoftBank Group, Graphcore works alongside companies developing advanced technologies. Our teams bring together AI researchers, silicon designers, hardware and software engineers, and systems architects to solve complex technical problems across the computing stack. The Opportunity As Principal Storage Architect, you will define the high-performance storage architecture for Graphcore's AI computing and data center infrastructures. You will lead the design of local and distributed storage tiers that provide the throughput, availability, and predictable tail latency required for large-scale training and inference. Working within Advanced Architecture, you will address storage requirements for saving model states, offloading key-value caches, large datasets, and high-speed data-loading pipelines. You will combine hands-on performance engineering with Principal-level technical leadership across hardware, software, networking, systems engineering, automation, supply chain, and external technology partners. What You Will Do Define the end-to-end architecture and technology roadmap for local, disaggregated, and distributed storage across Graphcore AI server and data center platforms. Design storage topologies that optimize PCIe lane allocation, network-domain placement, and data paths among CPUs, AI accelerators, memory, and storage systems. Lead the architecture of storage control-plane and data-plane solutions, evaluating commercial and open technologies against performance, resilience, manageability, and lifecycle requirements. Profile and tune the Linux storage stack, block layer, I/O schedulers, direct I/O paths, file systems, and drivers to imp

About the Team OpenAI’s Hardware organization develops system and infrastructure solutions tailored to the demands of advanced AI workloads. We work across the full stack—from silicon to system integration—partnering closely with internal teams and external vendors to define and deliver next-generation AI infrastructure. Our team focuses on defining scalable, high-performance system architectures and reference designs that balance performance, cost, and operational efficiency across rapidly evolving technologies. About the Role We are seeking a 3P Architect to define and drive rack- and cluster-level reference designs in collaboration with external partners. This role is responsible for translating workload requirements and system-level goals into concrete architectures, aligning partners on critical design attributes, and ensuring vendor roadmaps meet our infrastructure needs. You will work closely with performance modeling and internal architecture teams to evaluate tradeoffs, while owning the end-to-end definition and execution of third-party system designs. This includes identifying gaps in current technologies, driving vendor development, and shaping future infrastructure capabilities. This role requires strong system intuition, cross-functional leadership, and the ability to operate effectively across internal teams and external ecosystems. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance. Key Responsibilities Define rack- and cluster-level reference architectures for AI infrastructure deployments. Translate workload requirements into clear system design specifications and partner deliverables. Collaborate with performance modeling teams to evaluate architectural tradeoffs and system behaviors. Align internal stakeholders and external partners on critical system attributes (performance, cost, power, reliability, scalability). Identify gaps in current technology offerings and dr

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. We are looking for a talented engineer to join our CPU design team to drive infrastructure for performance analysis, correlation and verification. You’ll work on a CPU based on RISC-V ISA, collaborating with core architects and RTL teams to deliver a highly efficient and performant design. This role is hybrid, based out of Austin, TX or Santa Clara, CA. We welcome candidates at various experience levels. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are Strong problem solver who can identify complex issues, develop practical solutions, and drive them through implementation. Enjoy building tools and improving the infrastructure, workflows, and methodologies that make CPU development more efficient. Skilled in C++ and Python, with experience using industry-standard compilation, simulation, and emulation tools. Experience with verification infrastructure and methodologies along with knowledge of high-performance CPU architecture and microarchitecture. Proficient in debugging RTL and logic across multiple design hierarchies and pre-silicon environments. What We Need Join a team driving improvements in CPU microarchitecture, verifying performance, and correlating results between RTL and the performance mode

Who We Are Verkada is transforming how organizations protect their people and places with an integrated, privacy-sensitive AI-powered platform that includes solutions for video security, access control, air quality sensors, alarms, intercoms, and visitor management. We’ve got serious momentum in the market: more than 30,000 customers (including 100+ of the Fortune 500), a $5.8B valuation , more than $1 billion in annualized bookings, and backing from CapitalG, Sequoia Capital, General Catalyst, Felicis Ventures, Next47 and more. Physical AI is one of the most consequential technology shifts of our time, and Verkada is at the center of it. You can look at all kinds of communities to see our platform’s impact in the world. It's the retailer that uses our agentic AI to deter theft before it happens. The warehouse that uses AI-powered alerts to make sure its team is protected on the floor with proper PPE. The school that’s alerted to a threat in real-time and triggers a lockdown in seconds, not minutes. We’re rapidly scaling this impact: today, more than 2 million Verkada devices are deployed across 170+ countries. About the Role We are looking for a founding Staff Engineer to lead the technical architecture of our next-generation Spatial Platform. For the past decade, floor plans have been static images; we are rebuilding that foundation from the ground up as a high-performance, interactive, and collaborative 3D engine. This is a greenfield role for a builder who wants to own a core pillar of Verkada’s infrastructure before we hit the public markets. You won't just build features—you will architect a platform that supports 8+ product lines, solving the "hard math" of geospatial projections and the "hard graphics" of rendering thousands of interactive assets at a buttery-smooth 60 FPS. What You'll Do Build the Rendering Platform: Design a high-performance rendering architecture that supports both an interactive viewer for high-density geospatial visualizatio

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Join Micron’s AI-Optimized Memory Architecture team, where we are shaping the future of artificial intelligence workloads through next-generation memory solutions. We partner closely with customers to architect and deliver high-performance memory systems that enable emerging AI platforms and applications. Our team values innovation, technical excellence, and collaboration as we bring advanced memory technologies from concept to silicon. As a Member of Technical Staff, you will play a key role in translating architectural vision into efficient, silicon-proven hardware implementations. This position offers the opportunity to work on tightly coupled, high-performance memory architectures for AI, balancing performance, power, area, and reliability while influencing critical design decisions across the development lifecycle. Responsibilities: Translate architecture requirements into scalable microarchitectures and drive implementation from concept through silicon Design, implement, and review SystemVerilog RTL for advanced memory system architectures Perform synthesis and early PPA analysis to optimize area, power, and timing Develop and maintain microarchitecture documentation that aligns design intent across teams Evaluate architectural and implementation tradeoffs to support technical decision-making Minimum Qualifications: Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. As part of the Cloud Memory Business Unit (CMBU), this team advances Micron’s leadership in High Performance Memory solutions supporting Artificial Intelligence (AI), Machine Learning (ML), and High Performance Computing (HPC). The organization operates at the forefront of memory innovation, partnering closely with customers to enable next-generation computing capabilities. The Product Manager is responsible for defining and delivering the strategy for a High Performance Memory product portfolio. This role is critical to expanding Micron’s leadership in the HBM market by aligning product direction with customer needs, market trends, and emerging technologies, while driving cross-functional collaboration to deliver competitive solutions. Responsibilities Analyze emerging trends in AI hardware and evaluate their implications on high-performance memory architectures Define and drive product strategy and roadmap for high-performance memory products, ensuring alignment with market requirements and business goals Engage with customers, industry partners, and internal teams to gather insights on evolving AI/ML workloads and translate them into product requirements Assess the impact of next-generation AI workloads on future memory architectures and influence long-term product direction Support AI accelerator market analysis, including development of Total Addressable Market (TAM) and Serviceable Availab

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

Prototype. Experiment. Build Gallup’s next generation of products. As a senior software engineer at Gallup, you’ll push the boundaries of what our technology can do. You’ll design and deliver full-stack solutions, use rapid feedback loops to test ideas and help turn successful prototypes into scalable, production-ready systems. Your code, ideas and technical decisions will shape solutions that measure people’s work and life experiences, influence leaders’ decision-making, and deliver insights to customers more frequently and in a more personal way. If you’re driven by curiosity, collaboration and the chance to create technology that empowers millions around the world, apply today. What You’ll Do Design, build and ship experimental product features end-to-end, from database schema and API endpoints to UI components and deployment pipelines Write production-quality code across the stack: Node.js/Python backends, data models, RESTful APIs and modern JavaScript front ends with hands-on debugging and performance optimization Prototype with emerging AI technologies (e.g., LLMs, embeddings, agents) by implementing working integrations, running experiments and providing technical recommendations based on real performance data Architect and implement scalable systems from scratch, making concrete decisions on frameworks, infrastructure, caching strategies and deployment approaches What Makes You Stand Out Technical leadership: You’ve guided teams through complex, fast-moving projects. Tool versatility: You’re fluent in multiple languages and frameworks and know how to choose the right tool to deliver results. Collaborative mentorship: You share knowledge generously, explain technical concepts clearly and help others level up their expertise. Startup drive: You thrive among high achievers who believe experimentation and iteration fuel progress. What You Need Bachelor’s degree in computer science, computer engineering or a related field, or equivalent experience, required At l