Systems Administrator in United States

About the Team The OpenAI 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 As a Research Engineer, Distributed Data Systems, you will design and scale the infrastructure that powers large-scale multimodal training and evaluation at OpenAI. You’ll manage distributed data pipelines, collaborate closely with researchers to translate requirements into robust systems, and harden pipelines that serve as the backbone for OpenAI's rapid iteration cycles. We’re looking for engineers who are detail-oriented, have strong experience with distributed systems, and excel at building reliable infrastructure in high-stakes environments. 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 to new employees. In this role, you will: Design, build, and maintain data infrastructure systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure, machine learning infrastructure while ensuring scalability, reliability, and security. Ensure our data platform can scale by orders of magnitude while remaining reliable and efficient. Partner with researchers to deeply understand requirements and translate them into production-ready systems. Harden, optimize, and maintain critical data infrastructure systems that power multimodal training and evaluation. You might thrive in this role if you: Have strong experience with distributed systems and large-scale infrastructure with a strong interest in data. Are detail-oriented and bring rigor to building and maintaining reliable systems. Demonstrate excellent software enginee

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. Curious about how cutting-edge hardware actually comes to life? We're looking for someone who’s excited to dive into the core of next-gen systems and help make them real. In this role, you’ll validate high-speed interfaces, solve complex system-level puzzles, and collaborate across teams to shape the future of AI/ML computing. If firmware, hardware, and hands-on debugging sound like your kind of fun — let’s chat! This role is hybrid and based in Vancouver, Canada. 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 You love bringing hardware to life and enjoy the thrill of solving tricky problems across the hardware–firmware boundary. You’re hands-on in the lab and comfortable with tools like oscilloscopes, protocol analyzers, and JTAG — digging deep doesn’t scare you. You’re comfortable jumping into unfamiliar problems and figuring things out — whether it’s in the lab or in firmware. You’re curious, collaborative, and excited to work on technology that pushes the boundaries of performance. What We Need Someone to take the lead in validating our next-gen PCIe interfaces — from controller to PHY, and everything in between. A strong co

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 senior High Speed Interconnect / Signal Integrity Engineer to design and validate high-bandwidth links for large-scale AI systems. You will define, model, and qualify interconnect solutions across copper and optical technologies for next-generation AI inference and training clusters. This role is on-site in Santa Clara, CA, Austin, TX, or Toronto, Canada. 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 An experienced electrical engineer with a Bachelor’s or Master’s in Electrical Engineering. 5+ years working on high-speed communications (100G–1.6T), including signal integrity, channels, and links. Comfortable building and owning link and channel budgets and making clear tradeoffs between reach, loss, BER, and margin. Hands-on with SI tools and lab equipment such as Keysight ADS, VNAs, TDRs, BERTs, and protocol analyzers. Familiar with cable specification and testing, as well as accelerated life testing, mating life, and failure analysis. Able to collaborate across hardware, systems, and manufacturing teams; manufacturing/DFM/DTM experience is a plus. What We Need Define and specify high-speed interconnect architectures

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 highly skilled and experienced Engineer to lead post-silicon power characterization and correlation activities for cutting-edge semiconductor products. In this role, you will be responsible for developing and executing detailed power measurement strategies on silicon, correlating results with pre-silicon models, and driving improvements across power architecture, design, and modeling methodologies. You will serve as a key technical leader, interfacing across design, architecture, validation, and systems teams to ensure silicon meets power and performance specifications under all operating conditions. This role is hybrid, based out of Toronto, ON or Austin, TX 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 A Principal-level engineer with 8+ years in silicon power analysis and characterization, and a Master’s or PhD in EE, CE, or related field. Deep understanding of digital and mixed-signal power domains, including DVFS, leakage vs. dynamic power, and power gating. Highly proficient in lab-based power measurement using oscilloscopes, current probes, power analyzers, and SMUs, plus Python/Perl/MATLAB

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 Signal Integrity Engineer to join our growing team. The ideal candidate will have a wealth of exposure designing high speed interconnects, breakout design, material trade-offs and verification. A background in electrical engineering, electronics or relevant fields is required. Must love all things high speed! This role is hybrid, based out of Santa Clara, CA or Austin, TX or Toronto, ON. 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 You have a Bachelor’s degree in Electrical Engineering (or equivalent) and 5+ years working in high-speed digital design with a focus in high-speed PCB or package design at 10Gbps and above (e.g. 100GbE, GDDR6, PCIe Gen5+). You’re comfortable working with high-speed performance metrics such as ICR, ERL, COM, NEXT, and FEXT as well as knowledge of high-speed connector technologies, including NPO, CPO, and emerging standards. You are proficient with PCB ECAD tools (ideally Cadence Allegro). You communicate clearly (written and verbal), think critically, and love solving complex signal integrity problems. You’re enthusiastic about all things high speed and enjoy collaborating acro

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. This role sits at the center of cutting-edge AI hardware development, keeping the servers, PCIe systems, and engineering infrastructure running that power next-generation compute. You’ll be hands-on with rapidly evolving prototype and production systems, installing, maintaining, and troubleshooting hardware in fast-paced R&D and data center environments. Acting as a critical bridge between hardware engineers, software teams, and IT, you’ll help ensure seamless access to the platforms that turn ideas into working silicon and systems. This role is onsite, based out of Toronto, Canada or Austin, Texas. 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 A hands-on hardware professional who enjoys building, maintaining, and troubleshooting complex computing systems. Comfortable working in fast-paced R&D environments where hardware, firmware, and software are constantly evolving. Knowledgeable in computer architecture, operating systems, and hardware diagnostics, with strong problem-solving skills. Collaborative, detail-oriented, and motivated to improve processes through documentation, scripting, and automation. What We Need Inst

About the Team The Frontier Assurance team brings independent scrutiny into OpenAI’s safety decisions and helps the public understand and assess our safety work. We lead third-party assessments and safeguard testing for OpenAI’s flagship launches, pilot new assurance mechanisms such as embedded auditing, run our misalignment disclosure process, and incorporate independent expert input as evidence for critical safety decisions. About the Role As a Research Program Manager on the Frontier Assurance team, you will build programs that bring independent expertise into frontier AI safety decisions and make the evidence behind those decisions understandable to the public. You will lead external research partnerships and third-party assessments, coordinate public safety documentation, and develop new approaches to independent scrutiny and transparency. Working across research, engineering, product, policy, and communications, you will help ensure external findings inform concrete decisions and that our public explanations accurately reflect the evidence, limitations, and remaining uncertainty. We’re looking for people with deep experience in research partnerships and program management with technical and research teams. This role combines partnership management, cross-functional coordination, an understanding of AI safety research, alignment, and evaluations, and strong communication skills. You will work with researchers and engineers within OpenAI and across the external community to initiate projects, set ambitious goals and milestones, and drive execution across multiple teams. 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 to new employees. In this role, you will: Design and run third-party assessment programs for frontier models and safeguards, including independent evaluations, adversarial testing, and new approaches such as embedded auditing. Work with researchers and external part

About the Team Safety Systems manages the complete lifecycle of safety efforts for OpenAI’s frontier models, ensuring our models are deployed responsibly and have a positive impact on society. Our work spans diverse research and engineering initiatives—from system-level safeguards and model training to evaluation and red-teaming—all aimed at mitigating misuse, misalignment, and maintaining our high bar for safety. We lead OpenAI's commitment to developing and deploying safe Artificial General Intelligence (AGI), fostering a culture of trust, responsibility, and transparency. Our goal is to continuously learn from deployments, distribute AI’s benefits widely, and ensure that powerful tools remain aligned with human values and safety considerations. Within Safety Systems, the Model Policy team works to ensure that frontier models behave safely and reliably in real-world environments by designing policies that define safe model behavior. Some of our publications include: Safety at every step OpenAI GPT6 System Card OpenAI Model Spec About the Role We’re hiring a Model Policy Manager to shape model behavior for U.S. government use, with a focus on national security applications. You’ll define nuanced policies and translate them into training and evaluation criteria, helping models navigate high-stakes scenarios while preserving their usefulness and capabilities. In this role, you will: Develop model policies that guide safe and useful behavior. Build evaluations, identify policy gaps and model failures, and use findings to improve policies and training. Work with research, engineering, and domain experts to support safe, reliable deployment. You might thrive in this role if you: Bring relevant experience in AI safety, policy, or risk assessment. Have strong judgment and can turn complex safety questions into clear, practical policies. Have the technical fluency to work hands-on with model data and evaluations. Are motivated by OpenAI’s mission and the responsible use of

About the Team Safety Systems manages the complete lifecycle of safety efforts for OpenAI’s frontier models, ensuring our models are deployed responsibly and have a positive impact on society. Our work spans diverse research and engineering initiatives—from system-level safeguards and model training to evaluation and red-teaming—all aimed at mitigating misuse, misalignment, and maintaining our high bar for safety. We lead OpenAI's commitment to developing and deploying safe Artificial General Intelligence (AGI), fostering a culture of trust, responsibility, and transparency. Our goal is to continuously learn from deployments, distribute AI’s benefits widely, and ensure that powerful tools remain aligned with human values and safety considerations. Within Safety Systems, the Model Policy team works to ensure that frontier models behave safely and reliably in real-world environments by designing policies that define safe model behavior. Our relevant publications include: Safety at every step OpenAI GPT6 System Card OpenAI Model Spec GPT-Live ChatGPT Images 2.5 About the Role We are hiring a Model Policy Manager to focus on the safety of multimodal models. In this role, you will shape how OpenAI identifies, evaluates, and addresses risks in multimodal AI models - such as GPT-Live and ChatGPT Images - as well as multimodal capabilities in frontier AI models. 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 to new employees. In this role, you will: Design and maintain model policies for audio, image, video, and omni-modal behavior. Translate theories of harm and threat models into behavioral safety policies, evaluation criteria, grading guidance, and safeguards. Identify and analyze safety regressions and failure patterns to identify gaps in existing policies and inform policy iteration. Develop policy artifacts that support model training, evaluation, and deployment, including behavior i

About the Team Our Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. Within Safety Systems, the Model Policy team works to ensure that increasingly capable models behave safely and reliably in real-world environments. We investigate emerging model failures, define the behavior models should exhibit instead, and develop the data, evaluations, monitoring, and safeguards needed to improve and validate that behavior. Our work connects alignment research with the practical challenges of training and deploying frontier models. About the Role In this role, you will shape how OpenAI understands and addresses real-world risks that emerge from model misalignment as models become more autonomous and operate over longer horizons. You will investigate how misaligned behavior emerges across extended trajectories - including when models persist toward the wrong objective, take unsafe shortcuts, lose track of instructions, exploit weaknesses in their environment, or circumvent constraints - and translate these insights into behavioral policies, evaluations, monitoring, and safeguards. This role is ideal for someone who wants to turn alignment and safety concerns into concrete, empirically grounded improvements to frontier AI systems. Your Responsibilities: Identify vulnerabilities that emerge as models interact with tools, data, and external systems, and translate them into model- and system-level safeguards. Develop threat models and empirical frameworks for understanding harmful outcomes from misaligned behavior. Build frameworks for understanding harmful outcomes arising from model misalignment. Identify the underlying behaviors and system conditions that drive those outcomes. Turn findings into policy frameworks, evaluation criteria, online measurement and safeguards. Develop human data campaigns and gold sets to ground measurement and evaluation of eme

About the Team The Safety Training research team aims to fundamentally advance our capabilities for precisely implementing safe behavior in AI models, and to leverage these advances to make OpenAI’s deployed models safe and beneficial. This requires a breadth of new ML research to address the growing set of safety challenges as AI becomes more powerful and used in more settings. Key focus areas include how to train nuanced safety behaviors, how to make the model robust to bad actors, how to address privacy and security risks, and how to make the model trustworthy in safety-critical situations. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. About the Role We’re seeking a researcher to train and evaluate models for U.S. government use, with a focus on national security applications. You’ll advance safety post-training and robustness, helping models follow nuanced policies while preserving their usefulness and capabilities. In this role, you will: Research and implement methods for safety training, reinforcement learning, and adversarial robustness. Develop evaluations, identify model failure modes, and use findings to improve training. Work with research, engineering, security, and policy partners to support safe, reliable deployment. You might thrive in this role if you: Bring 4+ years of relevant AI safety research experience, including RLHF, adversarial training, or robustness. Have a degree in computer science, machine learning, or a related field, and strong deep learning research or engineering skills. Have experience improving model safety for deployment and enjoy collaborative research. Are motivated by OpenAI’s mission and the responsible use of AI in safety-critical settings. Security Requirements Active TS/SCI clearance or equivalent. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefi

About the Team The Agent Safety team works to ensure that increasingly capable AI agents act safely, exercise sound judgment, and remain aligned with user intent. Our mission is to reduce the probability of severe unintended outcomes from increasingly capable AI agents while preserving their ability to act effectively and autonomously. Our work spans three areas: Training: Create training methods, environments and data that teach agents to make better decisions in consequential situations. We turn real-world failures into training signals that prevent similar incidents, and identify precursor behaviors and mitigations to address emerging risks. Measurements: Build evaluations and production metrics that identify emerging risks and measure whether our interventions work. Oversight: Develop oversight and system mitigation mechanisms that reduce harmful actions while preserving useful autonomy (for example future versions of auto-review ). About the Role We’re looking for strong executors with excellent judgment, comfort with ambiguity, and an understanding of frontier model research. You don’t need prior safety or alignment experience, we also welcome people that recently realized that alignment and safety is a critical area to contribute to. 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 to new employees. In this role, you will: Train and evaluate frontier models to reduce harmful or misaligned agent actions, forming clear hypotheses and executing independently through ambiguity. Mine incidents and build scalable measurement, data-processing, and evaluation systems that turn real failures into repeatable safety signals. Collaborate closely with post-training, capabilities, oversight, and pre-training partners to ship research-backed mitigations into large-scale training and agent systems. You might thrive in this role if you: Have demonstrated strength in research engineering, ML en

About the Team The Agent Safety team works to ensure that increasingly capable AI agents act safely, exercise sound judgment, and remain aligned with user intent. Our mission is to reduce the probability of severe unintended outcomes from increasingly capable AI agents while preserving their ability to act effectively and autonomously. Our work spans three areas: Training: Create training methods, environments and data that teach agents to make better decisions in consequential situations. We turn real-world failures into training signals that prevent similar incidents, and identify precursor behaviors and mitigations to address emerging risks. Measurements: Build evaluations and production metrics that identify emerging risks and measure whether our interventions work. Oversight : Develop oversight and system mitigation mechanisms that reduce harmful actions while preserving useful agent autonomy (for example future versions of auto-review ). About the Role This role focuses on oversight and system-level mitigations that enable increasingly capable agents to operate safely and autonomously in real environments. We prioritize building oversight systems that are used in practice today, both internally and externally (see our recent work on action monitoring for codex and former code review ). We also study longer-term questions about how increasingly capable agentis systems can be supervised, constrained, and corrected. We’re looking for a safety&security minded researcher or engineer who can reason rigorously about security boundaries and agent behavior, then build and test practical mitigations. A background in AI control or security is welcome but not required. 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 to new employees. In this role, you will: Design, build, and evaluate system-level controls for agent actions like agent-based review. Plan how they fit in a broader syste

About the Team The Safety Systems org is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. The Safety Engineering team builds the platforms and tools that make OpenAI’s models safe to use in the real world. We partner closely with researchers, product teams, and policy to turn safety ideas into reliable, scalable systems: measuring risk, enforcing safeguards, and continuously improving how models behave in production. Our work sits at the intersection of product engineering, data, and AI, and directly shapes how millions of people experience OpenAI’s technology. About the Role We’re looking for a self-starter engineer who loves building products in an iterative, fast-moving environment—especially internal tools that unlock real-world impact. In this role, you’ll build full-stack tooling for our Safety Systems teams that directly improves the safety and reliability of OpenAI’s models, including in sensitive areas like mental health and other vulnerable-user protections. Your work will increase the team’s velocity in identifying and fixing safety issues and help tighten the feedback loop between policy, data, and the model training cycle. In this role, you will: Own the end-to-end development of internal tools that help improve the safety of OpenAI’s models (with a focus on areas like mental health and other vulnerable-user protections) Partner closely with Safety Systems researchers, engineers, and model policy creators to understand workflows, pain points, and requirements—and translate them into durable product solutions Build full-stack experiences to support core model policy workflows, such as labeling and inspecting data, analyzing and reviewing failure cases, and surfacing insights for iteration Optimize internal applications f

About the Team The Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. The Model Policy team aligns model behavior with desired human values and norms. We co-design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety. Key focus areas include: catastrophic risk, mental health, teen safety and multimodal safety. About the Role Providing access to frontier AI systems raises complex questions around dual-use science and catastrophic risk. How should models respond to requests involving chemical synthesis, biological experimentation, or pathogen research? Where is the boundary between legitimate scientific inquiry and information that could enable misuse? How do we design policies that meaningfully reduce risk without unnecessarily restricting beneficial research? This is a senior role in which you’ll help shape policy creation and development at OpenAI for addressing biological and chemical risks. You will develop structured policy frameworks and taxonomies to guide safe model behavior. This role sits at the intersection of biosecurity expertise, AI safety research, and policy design. You will help ensure that frontier AI systems can support beneficial life sciences research, such as drug discovery, public health, and biosafety, while reducing the risk that these capabilities could be misused. 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 to new employees. In this role, you’ll: Design and maintain model policies governing chemical and biological risk, defining how models should safely handle dual-use scenarios. Develop structured taxonomies of chemical and biological risk that inform model training data, evaluation benchmarks, and safet