Ai Infrastructure Systems Engineer Jobs

About the Team OpenAI's Industrial Compute organization builds and operates the infrastructure required to train and serve frontier AI models. The Capacity Planning team connects rapidly changing research and product demand with the compute, networking, storage, power, data center, hardware, and operational resources required to make that demand executable. About the Role We are seeking a Technical Program Manager to build and lead capacity planning across OpenAI's large-scale AI infrastructure. You will translate uncertain workload demand into clear infrastructure requirements, allocation decisions, supply commitments, activation priorities, and long-range capacity strategies. This role sits at the intersection of research, engineering, infrastructure, finance, sourcing, deployment, and operations. You will create the planning models, operating cadences, governance mechanisms, and source-of-truth systems that allow teams to understand what capacity is required, what is available, what is at risk, and what decisions must be made. This is not a finance-only forecasting or reporting role. Success requires technical fluency across the infrastructure stack, strong analytical judgment, and the ability to move consequential decisions forward when requirements, timelines, and supply conditions change quickly. Key Responsibilities Own capacity-planning processes across near-term workload allocation, quarterly execution, and longer-range infrastructure horizons. Translate research, training, inference, and product demand into compute, accelerator, cluster, networking, storage, rack, power, and site requirements. Develop scenarios that make assumptions, confidence levels, constraints, sensitivities, and decision points explicit. Reconcile requested demand against contracted, delivered, installed, activated, and workload-usable capacity. Partner with research and engineering teams to understand workload priorities, technical dependencies, utilization patterns, and changing req

The goal of a Staff Machine Learning Engineer at Scale is to lead the design and deployment of agentic AI systems that operate in real-world, mission-critical government environments. On the Public Sector team, you’ll work at the intersection of agentic ML, systems engineering, and applied research, building foundational infrastructure that enables AI systems to reason, plan, and act reliably at national scale. Our Public Sector ML Team partners directly with U.S. defense and intelligence agencies to deploy AI into classified and regulated environments. Through flagship programs like Donovan and Thunderforge , we are advancing the next generation of agentic AI for geospatial reasoning, planning, and decision support. Staff Machine Learning Engineers play a central role in setting technical direction, owning core architectures, and translating ambitious ideas into production systems trusted by government operators. You will: Lead the architecture and implementation of agentic AI systems, with a focus on long-horizon reasoning, orchestration, and system-level reliability. Build and scale agents that perform complex geospatial reasoning, including interpreting, generating, and reasoning over maps and spatial data. Design and improve retrieval systems across large collections of static and semi-structured documents, enabling agents to surface high-signal context efficiently. Fine-tune and evaluate embedding models to improve recall and precision for mission-critical datasets. Design memory systems that allow agents to persist state, operate over long contexts, and learn from prior interactions. Own and evolve shared agentic infrastructure and core libraries, enabling reuse across teams, products, and Public Sector contracts. Define evaluation strategies for agentic systems, including robustness testing, failure-mode analysis, and regression testing in production environments. Partner closely with engineering managers, product leaders, and researchers to scope high-impac

About the Role At Together AI, you'll own the systems and tooling that let our engineers ship quickly and reliably: CI/CD pipelines, release infrastructure, local dev environments, and the testing systems the whole engineering team depends on. This is a high-leverage, build-heavy role. You'll take on big projects and drive them end to end: building a full-stack release service with deployment orchestration, rebuilding the front-end build pipeline, standing up testing infrastructure that kills flaky builds. The goal is straightforward: engineers should spend their time building products, not fighting their tooling. This is an engineering role focused on building systems, not a QA or manual testing role. You build the infrastructure that makes manual effort unnecessary. Responsibilities Build and own the production systems engineers rely on: release services with deployment orchestration (canary, blue/green), reusable CI/CD pipelines, and self-service tooling. Build testing infrastructure: the frameworks and harnesses that make tests fast and reliable, and track down flaky tests and slow builds at the systems level. Build and maintain local dev environments and containerized workflows so engineers can spin up and iterate fast. Create starter templates and shared tooling (CLIs, codegen, IDE integrations) so teams can stand up new services in minutes. Be the go-to engineer for GitHub Actions and GitOps: defining problems, untangling pipeline issues, and improving how the org works with them. Improve front-end build tooling and developer workflows alongside product engineers. Work across the org to find the biggest sources of friction and fix them at the root. Document what you build so others can use it. Requirements 5+ years building software in production, with a strong track record of owning systems or services end to end, from design through operation. Strong software engineering fundamentals. You write production-quality code and have built and operated real servic

About the Role As a Hardware Security Engineer, you’ll contribute to building open, transparent, and secure AI systems. You’ll have the opportunity to work on projects that safeguard corporate assets by implementing and operating preventative and reactive security controls while collaborating with teams and stakeholders across the company. We’re looking for an intern that is interested in building AI-assisted security apps. Responsibilities Design, prototype, and implement security solutions for AI infrastructure across hardware, firmware, operating systems, and cloud services, including BMCs, TPMs, secure boot chains, networking interfaces, and workload identity systems. Assess and improve the security posture of server and accelerator platforms, including firmware update processes, device identity, trusted execution, and hardware-backed cryptography. Support the secure platform lifecycle, from device onboarding, provisioning, and inventory through configuration validation and decommissioning. Build automation and internal tools in Python, Bash, or similar languages to collect platform telemetry, detect configuration drift, and support incident investigation. Build AI-assisted security applications for asset classification, anomaly detection, and detection-and-response workflows. Partner with IT and infrastructure teams to implement and enforce identity, access-control, endpoint, and privileged-access security practices. Research and evaluate emerging technologies such as confidential computing, remote attestation, hardware roots of trust, and secure device management. Write clean, well-documented code, technical designs, test plans, and security findings. Requirements Actively pursuing a degree in Electrical Engineering, Computer Science, Software Engineering, or a related field expected by Summer 2027 Foundational understanding of computer architecture, operating systems, networking, embedded systems, digital logic, or electronics Strong programming skills

About the Role As a Hardware Security Engineer Intern, you’ll contribute to building open, transparent, and secure AI systems. You’ll have the opportunity to work on projects that safeguard corporate assets by implementing and operating preventative and reactive security controls while collaborating with teams and stakeholders across the company. We’re looking for an intern that is interested in building AI-assisted security apps. Responsibilities Help design, prototype, and assess security solutions for AI infrastructure across hardware, firmware, operating systems, and cloud services, working with components like BMCs, TPMs, secure boot chains, and device and workload identity systems. Support secure platform lifecycle work such as device onboarding, provisioning, inventory, and configuration validation. Build automation and internal tools in Python, Bash, or similar languages to collect platform telemetry, detect configuration drift, or support incident investigations. Prototype AI-assisted security tools for tasks like asset classification, anomaly detection, or detection-and-response workflows. Research an emerging area such as confidential computing, remote attestation, or hardware roots of trust, and share your findings with the team. Own a well-scoped project from start to finish with mentorship from experienced engineers, delivering clean, well-documented code and presenting your results at the end of the internship. Requirements Bachelor’s degree (or equivalent) in Electrical Engineering, Computer Science, Software Engineering, or a related field, earned or expected by Summer 2028 Foundational understanding of computer architecture, operating systems, networking, embedded systems, digital logic, or electronics Strong programming skills with a proficiency in Python or bash Exposure to building AI-assisted applications Preferred Qualifications Prior security engineering experience through internships, hackathons, or projects Coursework, projects, resea

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We are seeking a strong Senior Full-Stack Engineer to help us build, scale, and refine our rapidly growing product. The ideal candidate is deeply grounded in software engineering best practices and experienced in developing and scaling modern web applications end-to-end. You will work across the stack—from React/TypeScript frontends to Python-based backends—while integrating with LLMs and machine learning systems. You will solve complex challenges in scalability, reliability, and product experience while owning significant product areas in a fast-paced environment. What You’ll Do Own major full-stack product areas , driving features from design through production deployment. Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness. Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components. Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences. Deliver experiments and new features quickly , maintaining high quality and tight feedback loops with customers. Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP. Adapt quickly —learning new technologies, frameworks, and tools as needed across the stack. Ideal Experience 5+ years of full-time engineering experience , post-graduation. Strong experience developing full-stack applications using React, TypeScript, and Python . Experience scaling or shipping products at high-growth startups . Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome). Proficiency with SQL and modern API development. Experience with Kubernetes , containerization, and microservice architectures. Experience working with at leas

About the Role Together AI is building the AI Acceleration Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art AI cloud infrastructure. The AI Infrastructure team at Together AI is at the forefront of building and scaling the foundational systems that power our generative AI platform. The storage and observability team is crucial for designing, implementing, and maintaining robust distributed storage solutions, ensuring seamless data access and management. They are also responsible for developing comprehensive observability platforms, providing critical insights into system performance and GPU utilization, and proactively identifying and resolving issues. Responsibilities Design and implement a scalable observability platform (metrics, logs, traces) using tools like Prometheus, Grafana, ClickHouse, ClickStack, and OpenTelemetry, including telemetry data pipelines and log aggregation workflows. Develop automated monitoring, alerting, and anomaly detection systems, including SLIs/SLOs, runbooks, and predictive analytics for critical services. Build and deploy custom observability tools and infrastructure-as-code using Go, Python, Terraform, Ansible, and Helm. Collaborate with engineering teams to enhance distributed tracing and application monitoring, and lead incident response with post-mortem analysis. Define observability best practices. Requirements Expertise in observability platforms (Prometheus, Grafana, ClickStack, OpenTelemetry) and cloud-native monitoring services (AWS, GCP, Azure). Strong programming skills in Go, Python, or similar languages, with proficiency in infrastructure-as-code tools (Terraform, Ansible, Helm). Experience designing, operating, and scaling large-scale distributed systems and pipelines for high-volume data ingestion and real-time querying. Deep understanding of containerization (Docker) and orchestration (Kubernetes). Knowledge of microservices architectu

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes: Create multi-layered guardrails around agents Optimize data retrieval for agents Orchestrate fleets of asynchronous agents Automatically alerts users to deviations in data Illustrating how an agent reached a decision As a Software Engineer, you will own the development of a vertical feature or a horizontal capability to include defining requirements with stakeholders and implementation until it is accepted by the stakeholders. You will: Design and implement scalable backend systems for Federal customers using cloud-native AI infrastructure. Build features for agentic systems including multi-layered guardrails and data retrieval optimization. Develop data pipelines and machine learning infrastructure to make data sources accessible by agents. Collaborate with cross-functional teams to execute backend solutions for secure environments. Participate in customer engagements to understand requirements and deliver technical solutions. Define requirements with stakeholders and implement features until they are accepted. Contribute to the platform roadmap and product strategy for the Federal business. Ideally you will have: Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. For 10 years, Scale has provided the high-quality data and full-stack technologies that power the world's leading models, and has helped enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. Scale's internship is not a side project. Interns own real, shipped work on the same roadmaps as full-time engineers, with mentorship from world-class talent and a culture that values ownership, speed, and truth-seeking. Many of our interns return as full-time Scaliens. Example Projects Build reinforcement learning and post-training data pipelines that power frontier model development Develop evaluation infrastructure that measures model reliability for enterprise and public sector customers Ship agentic AI applications and the tooling that makes them observable, testable, and safe to deploy Ship tools that accelerate the growth of new qualified contributors on Scale's platform Build fraud-detection systems that remove bad actors and keep Scale's contributor base safe and trusted Use models to estimate the quality of tasks and contributors, and guarantee quality on requests at large scale Devise advanced matching algorithms that pair contributors to customers for optimal turnaround and accuracy Create optimized and efficient UI/UX tooling, in combination with ML algorithms, for 100k+ contributors completing billions of complex tasks Develop new AI infrastructure products to visualize, query, and explore Scale data Requirements A graduation date in Fall 2027 or Spring 2028 with a Bachelor's degree (or equivalent) in a relevant field (Computer Science, EECS, Computer Engineering, Statistics) Available for a Summer 2027 internship (May/June start dates) in San Franci

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. For 10 years, Scale has provided the high-quality data and full-stack technologies that power the world's leading models, and has helped enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. Every industry is moving AI to the center of operations, but there is still no clear blueprint for getting from pilot to profit. That is the problem our engineers work on. As a New Grad Software Engineer, you will own meaningful, customer-facing work from day one — shipping products, talking directly to the customers who use them, and iterating fast. Example Projects Build reinforcement learning and post-training data pipelines that power frontier model development Develop evaluation infrastructure that measures model reliability for enterprise and public sector customers Ship agentic AI applications and the tooling that makes them observable, testable, and safe to deploy Ship tools that accelerate the growth of new qualified contributors on Scale's platform Build fraud-detection systems that remove bad actors and keep Scale's contributor base safe and trusted Use models to estimate the quality of tasks and contributors, and guarantee quality on requests at large scale Devise advanced matching algorithms that pair contributors to customers for optimal turnaround and accuracy Create optimized and efficient UI/UX tooling, in combination with ML algorithms, for 100k+ contributors completing billions of complex tasks Develop new AI infrastructure products to visualize, query, and explore Scale data Build a customer service RAG application that handles thousands of questions a day Integrate a cutting-edge ML model that predicts churn into a customer's retention system

Scale AI is seeking a highly skilled and motivated Software Engineer, ARC (Architecture, Reliability, & Compute) to join our dynamic Public Sector Engineering team. As a part of this team, you will define how the company ships software, establishing the patterns for deploying into complex government and high-security environments, rather than just running Terraform scripts. You will build and maintain internal CLIs/tools that standardize testing, deployment, environment management and are tools that engineering relies on to prevent downstream breakages. You will execute on automated deployment efforts to pay down tech debt, creating fully functional staging/testing environments, and defining the company's standard for safe deployments. You will: Design and implement secure scalable backend systems for Public Sector customers, leveraging Scale's modern and cloud-native AI infrastructure. Own services or systems and define their long-term health goals, while also improving the health of surrounding components. Re-architect the stack to run in compliant or restrictive environments. This requires designing swappable components (auth, storage, logging) to meet government/security mandates without breaking the product. Collaborate with cross-functional teams to define and execute the vision for backend solutions, ensuring they meet the unique needs of government agencies operating in secure environments. Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions. Contribute to the platform roadmap and product strategy for Scale AI's Public Sector business, playing a key role in shaping the future direction of our offerings. Must have: At least an active secret clearance and the ability & willingness to up level to TS/SCI with CI Poly. This is a requirement and candidates will not be considered who do not hold at least a secret clearance Ideally you'd have: Full Stack Development: Prof

Scale AI is seeking a highly skilled and motivated Team Lead for our ARC (Architecture, Reliability, & Compute) team supporting our dynamic Public Sector Engineering team. As the lead of this team, you will define how the company ships software, establishing the patterns for deploying into complex government and high-security environments, rather than just running Terraform scripts. You will build and maintain internal CLIs/tools that standardize testing, deployment, environment management and are tools that engineering relies on to prevent downstream breakages. You will execute on automated deployment efforts to pay down tech debt, creating fully functional staging/testing environments, and defining the company's standard for safe deployments. As Team Lead, you will provide mentorship for the rest of the team and manage their performance while also guiding the technical direction of the team. You will: Design and implement secure scalable backend systems for Public Sector customers, leveraging Scale's modern and cloud-native AI infrastructure. Provide mentorship to and manage the performance of the other team members Own services or systems and define their long-term health goals, while also improving the health of surrounding components. Re-architect the stack to run in compliant or restrictive environments. This requires designing swappable components (auth, storage, logging) to meet government/security mandates without breaking the product. Collaborate with cross-functional teams to define and execute the vision for backend solutions, ensuring they meet the unique needs of government agencies operating in secure environments. Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions. Contribute to the platform roadmap and product strategy for Scale AI's Public Sector business, playing a key role in shaping the future direction of our offerings. Must have: At least an active sec

The IT Systems Engineering team builds the systems and processes that keep Asana running: endpoint management, identity, and the enterprise applications every Asana employee uses every day. As Manager of IT Applications and Client Platform Engineering, you’ll lead four engineers: two on client platform, two on applications. You report directly to the Head of IT Systems Engineering. This is a player/coach role: you set direction, run planning, and step in on escalations and architecture decisions while your engineers own most of the day-to-day. Your primary depth is in endpoint engineering; you have enough SaaS administration background to lead the apps side without micromanaging it. We’re looking for someone who knows endpoint work deeply and is ready to grow as a manager. Your title history matters less than technical foundation and how you work with people. This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you’re interviewing for this role, your recruiter will share more about the in-office requirements. What you’ll achieve Lead and develop your team. Manage and mentor four engineers (two on CPE, two on Apps). Set priorities, clear blockers, and help each person grow. Own the client platform program. Set strategy and lead execution for Asana’s endpoint fleet: macOS, Windows, and mobile. This means MDM infrastructure, zero-touch deployment, software catalog, and OS lifecycle. We’re primarily an Apple shop with a growing Windows footprint. Strengthen endpoint security. Work with the Corporate Security team to harden our endpoint posture, respond to incidents, and build initiatives that connect security priorities and endpoints. Oversee the applications engineering function. Your Apps engineers manage the

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE: We’re looking for exceptional storage & database engineers who want to join us on a new project to deeply integrate SpaceXSI's advanced AI infrastructure into a platform used by around 600 million users every month. We’re building a new storage tier that powers training, inference, recommendations, and real-time data extraction. We build and operate: An exabyte-scale S3-compatible object store A multi-tenant transactional key/value store that serves 100s of millions requests per second A caching tier that serves billions of requests per second A scalable vector database that powers our recommendation systems A few other specialized databases serving hundreds of millions requests per second RESPONSIBILITIES: Design, build, and launch to production new features and improvements aimed at unifying common components across the storage systems Dive into performance issues and work with customers and deliver solutions to cater to customers’ latency, availability and data durability requirements. Lead and drive incident responses and recovery with your peers. Review and contribute to incident postmortems and hold a high bar for the same. Work

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Developer Productivity group is responsible for making Stripe’s developers happy and productive. We work on tools, processes, and code refactoring to accelerate Stripe engineering as Stripe scales. We’re looking for people with an interest in building the tools to improve the day to day experience of Engineers in Stripe. The ideal candidate will have a passion for solving developer experience problems, and a pragmatic ability to ship results iteratively—powered by a mix of technical expertise across some or all of: language processing tools; version control systems; build systems; and distributed systems engineering. You’ll be working on a mix of engineer-facing systems, CI infrastructure platforms, and big-data engineering. What you’ll do We have a ton of important work to do, which is why we’re hiring! Our active projects change all the time, but here are a few examples of recent projects so you can get an idea of the types of work we do: Build and manage systems to handle CI at massive scale—including batching, speculative stacking, merge-race inhibition, and more. Build and manage systems to handle our enormous CI test suite–identifying and managing flaky tests, assessing and reproducing flakiness, and optimizing for test effectiveness. Enhance our CI systems for reliability, including adaptive response to available capacity, resilience and self-recovery from outages, and highly-leveraged observability. Detect and isolate code breaka