System Software Engineer - Performance Verification Infrastructure

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We’re looking for a software engineer to help build the design methodology, software abstractions, and infrastructure that enable a small silicon team to develop complex chips rapidly and with high confidence. You will turn evolving architecture and design needs into reusable tools and workflows that improve iteration speed, quality, and then apply those tools to help construct world-class silicon. You’ll work closely across architecture, design, verification, performance modeling, and systems software. This role is well suited for an engineer who enjoys building high quality software and is motivated by the challenge of improving velocity and quality of the silicon development process. In this role, you will: Develop and scale design methodologies for rapid first-party chip development and apply them to construct complex custom chips Create abstractions that allow hardware structures, configurations, experiments, and results to be represented consistently across tools. Automate high-value engineering workflows and improve their reproducibility, observability, testability, and ease of use. Partner with architects, RTL designers, verification engineers, compiler engineers, and systems software engineers to gather requirements and then implement solutions. Use methodology and tooling to identify design risks early, accelerate iteration, and improve confidence in performance and implementation tradeoffs. Contribute across multiple aspects of software and hardware

About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role We’re looking for a Robotics Control Systems Engineer to take on a foundational role within our robotics team. You’ll help architect, implement, tune, and verify the control infrastructure that enables intelligent, reliable, and responsive robot behavior. This is a deeply hands-on role focused on real-time systems, actuation, dynamics, low-level hardware interaction, and whole-robot performance. You’ll spend significant time working directly with robots onsite: debugging behavior, tuning subsystems, running experiments, and providing feedback across mechanical, electrical, and software teams. This role is based in San Francisco, CA, and requires in-person 5 days a week. In this role, you will: Design and implement real-time control algorithms for robotic systems, including motion control, feedback loops, state estimation, actuator control, and subsystem tuning. Define the control architecture from low-level actuators and hardware interfaces through whole-robot behavior and policy. Identify and characterize actuator, hardware, and software parameters through rigorous experimentation, testing, commissioning, and verification. Work with machine learning engineers to implement reinforcement learning models. Collaborate across mechanical, electrical, and software teams to integrate control logic with sensing and actuation hardware. Help inform the mechanical and electrical design to maximize capability and flexibility. Create the control system architecture; determine the correct level of abstraction from actuators all the way up to whole-robot

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. Tenstorrent is seeking a SoC Design Verification Engineer to validate the System Management Controller (SMC) and enable seamless multi-chip integration. In this role, you will design and execute tests, build infrastructure, and debug issues across chiplet-based SoCs. You’ll have the opportunity to work with remote mentorship while contributing to the foundation of scalable multi-die systems. This role is hybrid, based out of Toronto, Ontario, Boston, MA or Santa Clara, CA. We welcome candidates at various experience levels for this role. 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 Proficient in SystemVerilog, SV-UVM, Python, and C/C++ with strong verification skills. Experienced in writing test plans, building infrastructure, and debugging hardware/software flows. Comfortable working with remote mentorship and distributed teams. Familiar with AI-assisted tools like Copilot, Cursor, and Claude to accelerate verification. What We Need Develop and maintain SMC tests and supporting DV infrastructure. Write, execute, and track test plans for chiplet and multi-chip SoC designs. Use C/C++ to develop tests compiled, loaded, and executed directly on the DUT. Triage, analyze, and debug issues in clos

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. Our Tensix team is building custom AI compute cores, RISC-V CPUs, and chiplet-based architectures for datacenter, edge, and automotive AI. Design Verification Engineers on this team validate compute IP and subsystems and build scalable DV infrastructure to keep verification fast, automated, and production-grade. This role is hybrid, based out of Toronto, ON, Austin, TX or Belgrade, Serbia. We welcome candidates at various experience levels for this role. 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 Experienced in modern verification methodologies with strong SystemVerilog skills and exposure to structured testbench development. Comfortable working from block-level to system-level verification and reasoning about microarchitecture behavior from specs and waveforms. Proficient in Linux environments with Python, or Bash scripting for automate builds, parse logs, manage CI pipelines. Skilled in coverage-driven verification and confident debugging across RTL, testbench, and workload scenarios. Motivated by AI hardware and eager to learn verification strategies for new architectures and domains. What We Need Contribute to verification of Tensix IP and subsystems from early planning through tape-out, owning cov

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

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. We are building the next-generation data and storage infrastructure to solve some of the hardest problems in AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Our work gives NVIDIA teams the foundational capabilities they need to build, train, deploy, and operate AI products at scale without reinventing critical data infrastructure for every workload. What you will be doing: Build cloud-native data and storage services for hybrid and multi-cloud infrastructure, including dataset discovery, ingestion, governance, checkpointing, observability, and low-latency access. Develop scalable cloud-native services and APIs that support exabyte-scale, high-performance GPU training and inference workflows. Work closely with product managers, internal AI teams, platform teams, and partner engineering teams to understand requirements and turn them into reliable production systems. Collaborate with SRE, operations, and support teams to improve service reliability, performance, observability, on-call readiness, and operational scale. Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, and verification. What we need to see: BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience, with 5+ years of software engineering experience. Strong foundation in algorithms, data structures, distributed systems, and practi