Systems And Data Integration Lead in United States

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Company Overview We are building the next generation of cloud security infrastructure, focusing on advanced Data Exfiltration Protection (DXP) and unified Data Movement Policies (DMP). Our mission is to provide seamless, context-aware security that protects sensitive data without hindering developer velocity. We are looking for a visionary Principal Engineer to lead the technical strategy and architecture for our Data Movement and Perimeter control systems. Role Summary As a Principal Engineer in the Data Protection group, you will be the technical lead for the Data Exfiltration Protection (DXP) and Data Movement Policy (DMP) initiatives. You will bridge the gap between high-level security policy and low-level system enforcement, ensuring that our perimeter controls are robust, scalable, and deeply integrated with context-aware access policy frameworks. You will be responsible for the architectural evolution of our egress control systems, moving from simple IP-based rules to sophisticated, content-aware, and identity-driven data movement governance. AS A PRINCIPAL SOFTWARE ENGINEER - IDENTITY, DATA SECURITY AND TRUST AT SNOWFLAKE YOU WILL: Architectural Leadership: Lead the design and implementation of the Data Movement Policy (DMP) framework, ensuring it can handle complex

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

What you’ll do Design and implement secure cloud pipelines that ingest very large scan datasets (multi-terabyte), reliably and resumably. Build orchestration for GPU-accelerated reconstruction and analysis with strong retry semantics, idempotency, and cost controls. Define end-to-end data lifecycle for medical imaging: raw vs intermediate vs derived artifacts, retention policies, and reproducibility. Implement security + compliance primitives appropriate for HIPAA/PHI: encryption in transit/at rest, key management, least privilege, audit logs, and access reviews. Build operational tooling: monitoring, alerting, runbooks, and incident-driven improvements for a growing device fleet. What we’re looking for Strong experience with cloud batch/queueing/orchestration, storage systems, and data pipeline reliability. Experience shipping production systems that handle large data volumes and failure-prone networks. Practical security mindset (least privilege, secrets, audit logging) and comfort operating in compliance-constrained environments. Useful experience Building reliable data pipelines at scale (queues/orchestration, resumable uploads, GPU batch execution) with strong observability. Security + privacy by default: encryption, least-privilege access, auditing, and practical HIPAA/PHI guardrails. Owning the “boring” backend details that keep a lean team moving: schemas/migrations, cost controls, retries, and runbooks. Understanding compute tradeoffs across hardware options, and specifying appropriate cloud resources.

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 seeking a Security Engineer to join our First-Party Hardware team. In this role, you will own the end-to-end security foundation for OpenAI's first-party AI hardware systems, working across hardware security, embedded security, system security, and practical deployment at data center scale. You will partner with silicon, hardware, firmware, infrastructure, manufacturing, operations, and security teams to define and deliver system-level device trust. This includes boot integrity, device identity, provisioning, attestation, management-plane security, storage encryption, debug controls, firmware update and recovery, RMA, and decommissioning. You will be accountable for turning threat models into requirements, requirements into implementation, and implementation into validation evidence that can support launch decisions. Location: San Francisco, CA (Hybrid: 3 days/week onsite) Relocation assistance available. In this role, you will: Own security requirements, threat models, validation strategy, and launch-readiness evidence for first-party hardware platforms from early design through production deployment. Design and review secure boot, measured boot, roots of trust, platform firmware resilience, firmware signing, recovery, and anti-rollback strategies across heterogeneous devices. Own device identity, provisioning, enrollment, attestation, certificate lifecycle, and key-management requirements across manufacturing and data center bring-up. Harden management

About the Team OpenAI's data and storage infrastructure spans data platforms, online databases, and file/object storage. These systems underpin data ingestion and processing, durable persistence, indexing and retrieval, and product file experiences. As frontier models and agents evolve how they use memory, history and snapshots, the underlying architecture increasingly shapes the capabilities products can deliver—and their latency, reliability, cost and efficiency. About the Role We are looking for a technically deep TPM to independently define and lead multiple programs across data platforms, online databases and storage infrastructure. You will connect model, product and data-consumer requirements to architecture, and work with the relevant engineering teams to take new capabilities through production adoption and repeatable expansion. The design scope is exabyte-scale storage and infrastructure spanning multiple millions of CPU cores. The challenge is not simply forecasting more resources: it is making complete, workload-ready capacity repeatable, with a clear path from product requirements through architecture, deployment and validation. A data pipeline, database query, file operation or execution snapshot can affect whether a product or agent succeeds; you will connect those outcomes to the systems underneath. 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: Translate model, product and data-platform needs into precise access patterns, consistency, durability, freshness, availability and scalability requirements. Connect memory, history, retrieval and resumable work to capability and end-to-end latency. Partner with engineering to transform data and storage architecture into repeatable scale units: standardized provisioning, placement, routing, data movement and readiness checks that bring storage, compute and networking online together.

About the Team OpenAI’s Industrial Compute team is building and productizing infrastructure capabilities that help organizations deploy and operate advanced AI systems at scale. The team works across AI hardware, systems engineering, physical infrastructure, and customer delivery to turn emerging technologies into reliable, repeatable infrastructure solutions. Our work sits at the intersection of technical strategy, product development, engineering, and deployment. We partner closely with customers and internal engineering teams to solve complex infrastructure challenges spanning compute, power, cooling, controls, and facility efficiency. About the Role We are seeking a senior, hands-on Data Center Infrastructure Architect to develop and optimize the physical infrastructure required for large-scale AI deployments. This is a broad technical role spanning data center architecture, electrical and mechanical systems, high-density compute, controls, telemetry, and digital modeling. You will use simulation, operational data, and digital-twin approaches to evaluate infrastructure designs, identify system-level constraints, and improve efficiency, reliability, cost, and speed of deployment. The ideal candidate can move fluidly between first-principles analysis, facility and equipment design, computational modeling, engineering review, and real-world implementation. You should be comfortable working across disciplines rather than operating solely within electrical, mechanical, or software boundaries. Key Responsibilities Define system-level architectures for high-density AI data centers across power, cooling, IT equipment, controls, and facility infrastructure. Develop digital twins and other computational models that represent the behavior of data center systems under changing workloads, environmental conditions, equipment configurations, and failure scenarios. Use design and operational data to identify constraints, improve PUE and related efficiency metrics, and optimize

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

The Data Analyst, Revenue Operations is responsible for turning data from multiple source systems into the reporting, models, and visualizations that power revenue, sales, and operations decision-making. Reporting into the Revenue Operations organization, this role combines hands-on SQL and data modeling with predictive segmentation, opportunity analysis, and dashboard development — and serves as a trusted reporting partner to teams across the business. You will work primarily within Revenue Operations while collaborating cross-functionally with Sales, Business and Technical Operations, Sales Enablement, Support, and Finance to get things done. About NMI NMI, a global leader in payment technologies, allows software and sales professionals to deliver payment innovations to their clients without having to build payment expertise in house. NMI enables our partners with choice, and challenges the one-size-fits-all approach to payments. You've probably used NMI in the last 24 hours without even realizing it. We're the platform that powers success for innovative tech created by SMBs, entrepreneurs and fintech startups. We're creative problem solvers who help visionaries smash through boundaries and think beyond what's possible so they can think about what's next. But we're not just built for the tech savvy. We democratize the latest payments technology so that everyone can realize the benefits of easy payments across the full spectrum of commerce. Our mission is to use our expertise to enable more payments, in more ways and more places. We provide ISOs, Fintech Innovators and Technologists the freedom to focus on what they do best, liberating them from restricted payment solutions and giving them access to the latest payment technology now and in the future. How We Work - Our Core Values * Be a Good Person, It's Simple. We treat everyone fairly and with respect. We lead with kindness, humility and integrity. We take the time to understand each other and our customers. And

About Graphcore Graphcore is a global leader in artificial intelligence computing systems. We design advanced semiconductors and data center hardware that deliver the specialized processing power needed to advance AI while improving the efficiency required for broad adoption. As part of SoftBank Group, Graphcore belongs to a family of companies developing some of the world's most transformative technologies. Our AI Engineering Campus in Austin plays an important role in building the future of AI computing. The Opportunity As Technical Services Director, you will lead the teams that operate and evolve Graphcore's engineering labs, high-performance computing (HPC) platforms, and data center environments globally. You will be accountable for reliable, secure, cost-effective infrastructure that supports demanding engineering, AI, silicon-development, and validation workloads. This role combines people leadership, infrastructure strategy, operational excellence, capacity and financial planning, procurement, and program delivery. You will partner with Engineering, Information Technology, Security, Finance, Facilities, Supply Chain, customers, and external suppliers. The position is based onsite in Austin and requires travel to company facilities, data centers, and supplier locations, including international travel. What You'll Do Lead, recruit, mentor, and develop the systems administration, lab operations, and technical services teams responsible for the facility supporting global Engineering and Research and Development. Own the reliability, efficiency, protection, safety, supportability, and continuous improvement of engineering labs, HPC systems, and infrastructure facilities. Establish service levels, operating standards, escalation paths, performance measures, monitoring, observability, automation, ticketing, and configuration-management practices. Translate engineering and customer requirements into infrastructure roadmaps, capacity p

About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make rigorous, evidence-based talent decisions and ensures that the systems supporting those decisions are valid, reliable, fair, and accountable. About the Role As a People Data Scientist focused on AI fairness and bias testing, you will help establish how OpenAI evaluates AI-assisted People systems and high-impact talent processes. You will design and conduct rigorous assessments to identify, measure, and mitigate potential bias across the lifecycle of models, agents, decision-support tools, and automated workflows. Your work will span the entire employee life-cycle, such as hiring, performance, promotion, employee development, workforce planning, etc. You will evaluate both technical systems and the broader human-AI decision processes in which they operate, examining not only model performance but also data quality, measurement validity, differential outcomes, human oversight, and unintended consequences. We’re looking for an experienced data scientist or applied researcher who can translate complex fairness questions into defensible evaluation strategies, scalable testing infrastructure, and clear recommendations for technical teams and senior leaders. This role is preferred to be based in San Francisco, CA. In this role, you will: Define and lead fairness and bias-testing strategies for AI-assisted People processes, models, agents, and decision-support systems from development through deployment and ongoing monitoring. Design rigorous algorithmic audits and validation studies, including adverse-impact analysis, subgroup and intersectional evaluation, error-rate analysis, calibration, measurement invariance, reliability, criterion-related validity, and sensitivity testing. Identify the appropriate fairness criteria for each use case, evaluate tradeoffs among competing definitions

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE As an OS / K8s Systems Engineer at Baseten, you’ll build the automation and systems that turn raw GPU hardware into production-ready compute. From provisioning to orchestration, you’ll own the software layer that makes our infrastructure reproducible, scalable, and reliable across data centers. This is a senior, hands-on role focused on building systems not operating them. You’ll work close to the metal designing OS images, building provisioning pipelines, and automating cluster bring-up from scratch. Your work will define how quickly we can turn new capacity into usable compute. EXAMPLE INITIATIVES Zero-to-cluster automation Build workflows that take new hardware from unprovisioned to fully operational cluster. Provisioning systems Design PXE-based or equivalent systems for imaging and lifecycle management. Reproducible infrastructure — Ensure clusters deploy consistently across data centers. RESPONSIBILITIES Own the end-to-end automation of cluster bring-up and lifecycle management. Build and maintain OS images, provisioning systems, and configuration pipelines. Deploy and operate cluster orchestration platforms (Kubernetes, Slurm, or similar). Design systems for reproducibility across sites and hardware generations. Automate upgrades, rollouts, and failure recovery. Optimize system performance, including GPU utilization and networking. Partner with hardware and network teams to validate and improve system b

About the Team The Frontier Systems team at OpenAI builds, launches, and supports the largest supercomputers in the world that OpenAI uses for its most cutting edge model training. We take data center designs, turn them into real, working systems and build any software needed for running large-scale frontier model trainings. Our mission is to bring up, stabilize and keep these hyperscale supercomputers reliable and efficient during the training of the frontier models. About the Role As a Software Engineer on the Frontier Systems team focused on power management, you will work on critical infrastructure to support cutting-edge research. With large-scale supercomputers consuming substantial amounts of power, managing this efficiently is key to maximizing computational capacity. This role is critical to ensuring that our cutting-edge research supercomputing infrastructure runs smoothly, while maintaining reliability and grid-level power stability. Our team empowers strong engineers with a high degree of autonomy and ownership, as well as ability to effect change. This role will require a keen focus on system-level comprehensive investigations and the development of automated solutions. We want people who go deep on problems, investigate as thoroughly as possible, and build automation for detection and remediation at scale. In this role, you will: Develop and implement system-level and software-level solutions to optimize power usage in large-scale supercomputers, ensuring efficient and reliable operations. Build automation to monitor power consumption patterns during training workloads and design algorithms to stabilize these fluctuations, preventing issues with grid reliability. Work with researchers and engineers to design tools for real-time monitoring, detection, and remediation of power-related hardware and system faults. Collaborate cross-functionally to translate complex electrical system requirements into code, while driving continuous improvements in power man

About the Team The Statsig team at OpenAI builds and operates the experimentation platform that powers product development, measurement, and decision-making across the company. We partner closely with product, engineering, and infrastructure teams to ensure experiments are trustworthy, statistically rigorous, and scalable to the needs of frontier AI products. Our mission is to help teams make better decisions through reliable experimentation. We care deeply about statistical correctness, pragmatic solutions, and building systems that researchers and engineers can trust at massive scale. The team operates at the intersection of experimentation methodology, data infrastructure, causal inference, and product analytics. We are looking for experienced experimentation experts who want to shape the future of experimentation in the AI era. About the role: We're seeking a Data Engineer to take the lead in building our data pipelines and core tables for OpenAI. These pipelines are crucial for powering analyses, safety systems that guide business decisions, product growth, and prevent bad actors. If you're passionate about working with data and are eager to create solutions with significant impact, we'd love to hear from you. This role also provides the opportunity to collaborate closely with the researchers behind ChatGPT and help them train new models to deliver to users. As we continue our rapid growth, we value data-driven insights, and your contributions will play a pivotal role in our trajectory. Join us in shaping the future of OpenAI! In this role, you will: Design, build and manage our data pipelines, ensuring all user event data is seamlessly integrated into our data warehouse. Develop canonical datasets to track key product metrics including user growth, engagement, and revenue. Work collaboratively with various teams, including, Infrastructure, Data Science, Product, Marketing, Finance, and Research to understand their data needs and provide solutions. Implement ro

About the Team The Statsig team at OpenAI builds and operates the experimentation platform that powers product development, measurement, and decision-making across the company. We partner closely with product, engineering, and infrastructure teams to ensure experiments are trustworthy, statistically rigorous, and scalable to the needs of frontier AI products. Our mission is to help teams make better decisions through reliable experimentation. We care deeply about statistical correctness, pragmatic solutions, and building systems that researchers and engineers can trust at massive scale. The team operates at the intersection of experimentation methodology, data infrastructure, causal inference, and product analytics. We are looking for experienced experimentation experts who want to shape the future of experimentation in the AI era. About the Role We are hiring a Staff-level Data Scientist to help lead the evolution of OpenAI’s core experimentation platform. This role is focused on improving the statistical rigor, reliability, and practical usability of experimentation across the company. You’ll work on some of the hardest problems in online experimentation: sample ratio mismatch detection, variance reduction, bias mitigation, metric design, triggered analysis, heterogeneous treatment effects, sequential testing, and experimentation in complex ML systems. You’ll also help translate advanced statistical concepts into pragmatic systems and product experiences that teams can actually use. This is a highly technical individual contributor role with significant influence across methodology, platform architecture, and experimentation best practices. The ideal candidate combines deep statistical expertise with strong systems intuition and hands-on experience building or operating experimentation platforms at scale. In this role, you will: Drive the statistical direction and technical strategy for OpenAI’s experimentation platform Design and improve experimentation methodolo

About the Team The Intelligence and Investigations team seeks to rapidly detect and disrupt abuse in AI technologies to ensure their safe use. We are dedicated to identifying emerging abuse trends, analyzing risks, and working with our internal partners to implement effective mitigation strategies to protect against misuse. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits all of humanity. About the Role As an Intelligence Systems Engineer, you’ll be focused on advancing our Intelligence & Investigations efforts at OpenAI, ensuring the safe and responsible use of AI across our products and services. We are seeking a self-starter to prototype, develop, and maintain new tools and processes that integrate OpenAI’s models and infrastructure to enable internal teams to make sense of large, open-domain datasets, fight abuse, and inform high-stakes decisions. You will be a crucial technical bridge between our data scientists and subject matter experts and technical teams like Platform Integrity, Safety Systems, and Research by leading the development of innovative tools and processes that bolster goals in scaled collections, investigations, and analysis. The ideal candidate has strong analytical and data skills, with a background in both prototyping and building scalable systems that can swiftly detect emerging threats, process vast amounts of information, and deliver insights to stakeholders. We value professionals with outstanding communication skills, a commitment to continuous learning, and who are dedicated to promoting the responsible use of AI. 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: Prototype, build, and maintain at-scale intelligence systems that detect, triage, and monitor targeted signals from both open-source and internal data Analyze requirements and deliver end-to-end solutions that address