Software Engineer Ai Infrastructure Jobs

Software Engineer - AI Infrastructure

This is a greenfield environment where software engineers will have the opportunity to build systems from the ground up across distributed systems, data engineering, automation, developer tooling, AI enablement and product engineering. What you’ll do * Own the design and delivery of software components end to end, including interfaces, data models and storage * Design and build distributed systems that remain reliable under load, including handling partial failure, backpressure and recovery * Remain hands-on with coding, code reviews and technical design * Lead small groups of engineers through delivery, breaking down work, sequencing priorities and maintaining design consistency * Own the full production lifecycle of what you build, including testing, deployment, monitoring, on-call and incident follow-up * Work closely with product, engineering and operations teams to turn loosely defined requirements into scalable technical solutions * Contribute to CI/CD, observability and developer experience What we’re looking for * 3+ years of professional software engineering experience (in AI Infrastructure/Data Centers, Hyperscalers, leading tech/SaaS companies or Big 4 Banks - experience from these companies is paramount) * Strong proficiency in at least one programming language such as Python, Go, TypeScript, Java, C# or Rust * Experience designing software components and owning technical decisions * Practical experience building and operating distributed systems in production * Strong understanding of data structures, algorithms, concurrency and system design * Experience owning production services, including monitoring, on-call and incident response * Experience leading a small group of engineers through delivery, formally or informally * Strong written and verbal communication skills * Experience across data engineering, automation, developer tooling, AI/ML, cloud infrastructure, Kubernetes, Terraform, high-volume data pipelines or telemetry would be highly regarded. The role can be hybrid in Sydney, or fully remote if you’re based elsewhere in Australia.

Software Engineer - AI Tooling Infrastructure

From $100/yr

Accountabilities: Build, deploy, operate, and scale production backend services supporting AI-powered developer tools, including MCP servers, agent skills, and AI assistants. Own service architecture and infrastructure decisions, improving system reliability, maintainability, scalability, and deployment processes. Define and monitor service reliability objectives (SLOs), track error budgets, and prioritize improvements in collaboration with Site Reliability Engineering (SRE) teams. Participate in the team's on-call rotation, investigate production incidents, coordinate resolutions, and develop incident reports, runbooks, and preventive solutions. Manage operational support requests and identify recurring issues that can be addressed through long-term engineering improvements. Design and maintain authentication and authorization systems for AI agents and machine clients, including OAuth applications, scoped tokens, permissions, and secure access management. Establish and maintain comprehensive observability through metrics, distributed tracing, logging, dashboards, and alerting systems. Manage AI agent traffic, implementing rate limits, capacity controls, cost safeguards, and mechanisms to handle dependency failures and unexpected traffic spikes. Collaborate with Platform, SRE, Authentication, Billing, and Edge Functions teams to improve shared infrastructure, release processes, security, and operational practices. Contribute to the development of AI tooling infrastructure, ensuring that reliability, security, and performance remain central to engineering decisions. Requirements: At least 5 years of experience building, deploying, and operating production backend services, including participation in on-call rotations. Strong programming skills in TypeScript or Go, with practical working knowledge of the other language. Proven experience designing and maintaining APIs, including versioning, error handling, rate limiting, and backward compatibility. Strong understanding of OAuth, OpenID Connect (OIDC), JWT, and API authorization, with the ability to troubleshoot authentication and permission issues in production environments. Good knowledge of PostgreSQL and relational database systems. Experience with CI/CD pipelines, Docker, containerized deployments, and Infrastructure as Code (IaC). Ability to troubleshoot distributed systems using metrics, logs, and traces to identify performance bottlenecks and service failures. Strong written and verbal communication skills, with the ability to collaborate across teams and work effectively in globally distributed, asynchronous environments. Adaptability, ownership, and the ability to navigate ambiguity while delivering solutions in fast-paced engineering environments. Deep expertise in at least one of the following areas: APIs and Authentication: Experience designing permission models, token scopes, and OAuth flows for third-party integrations or machine clients, including safely migrating authentication systems without disrupting existing users. Production Operations: Hands-on experience managing production services, participating in on-call rotations, developing alerts and runbooks, implementing SLOs, and improving system reliability. Gateways and Distributed Systems: Experience building or operating proxies, gateways, or distributed services, troubleshooting retries, partial failures, upstream outages, and making appropriate fail-open or fail-closed decisions under degraded conditions. Nice to have: Experience with AI agent tooling, Model Context Protocol (MCP), or infrastructure serving AI agents and LLM clients. Experience extracting services from larger codebases while maintaining compatibility with existing integrations. Familiarity with observability platforms such as Grafana, OpenTelemetry, or Logflare, along with practical experience implementing SLOs. Contributions to open-source projects, particularly within the Node.js, Go, TypeScript, or API tooling ecosystems. Benefits: Fully Remote: Work from anywhere in the world, with no office attendance requirements. Co-working Allowance: Access to a WeWork membership or co-working budget to support your preferred working environment. Equity Ownership: Employee Stock Option Plan (ESOP), giving every team member an opportunity to share in the company's long-term growth. Technology Allowance: Dedicated budget for professional equipment, including laptops, monitors, headphones, and other work essentials. Health Insurance: 100% health insurance coverage for employees and 80% coverage for dependents, regardless of location. Annual Company Off-sites: Opportunities to connect and collaborate with colleagues during a company-wide gathering in a different city each year. Flexible Working Arrangements: Asynchronous work culture with flexibility to organize your schedule and manage your responsibilities independently. Professional Development: Annual learning and education allowance for courses, books, conferences, and other career development opportunities. Global Collaboration: Join an international, remote-first engineering team working across more than 60 countries and 20 languages. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1

Staff Software Engineer - Infrastructure (AI Native)

From $15/yr

Accountabilities Design, build, and improve scalable cloud infrastructure spanning Kubernetes, networking, compute, storage, and distributed systems. Identify performance bottlenecks, potential failure modes, operational risks, and infrastructure weaknesses before they affect production environments. Strengthen CI/CD pipelines, automated testing, deployment controls, and rollout strategies to support safer, more reliable releases. Build and enhance observability capabilities, including monitoring, alerting, distributed tracing, and operational tooling that improves system visibility and troubleshooting. Optimize infrastructure performance, reliability, resource utilization, and cloud expenditure. Design, develop, and deploy AI agents for infrastructure management and engineering workflows, enabling them to use tools and APIs, execute multi-step tasks, troubleshoot issues, and automate operational activities. Establish safeguards for AI agents operating in production, including evaluation frameworks, permission controls, guardrails, observability, state management, and failure recovery mechanisms. Build and maintain internal platforms and developer tools that improve engineering productivity, such as package registries, artifact stores, CI systems, and infrastructure automation solutions. Lead technical design discussions and contribute to architectural decisions, design documentation, code reviews, and engineering standards. Mentor engineers, share technical knowledge, and help raise the quality of implementation and operational practices across the team. Participate in the on-call rotation, respond to production incidents, and translate incident learnings into permanent fixes, runbooks, and automation that reduce recurring issues. Collaborate across engineering teams to build infrastructure that supports evolving AI workloads and enterprise-scale production requirements. Requirements: Demonstrated experience designing, building, and shipping AI agents or agentic systems that perform meaningful, real-world tasks. Experience limited to AI coding assistants or prompt experimentation is not sufficient. Strong understanding of infrastructure fundamentals, including distributed systems, cloud computing, Kubernetes, networking, and production operations. Experience taking infrastructure from early development stages to meaningful production scale, with the ability to explain architectural choices, trade-offs, and lessons learned. A reliability-focused engineering mindset, with a strong understanding of failure modes, blast radius, operational complexity, resilience, and recovery strategies. Ability to troubleshoot complex issues across multiple layers, from application behavior and distributed services to Kubernetes, networking, and cloud infrastructure. Experience automating repetitive operational tasks and developing tools or platforms that improve the productivity of other engineers. Experience building internal developer platforms or infrastructure systems, such as CI pipelines, artifact storage, package registries, or related tooling. Strong technical communication skills, including the ability to produce clear design documents, explain architectural decisions, lead technical reviews, and document operational learnings. Staff-level technical leadership skills, including mentoring engineers, influencing engineering standards, and guiding complex projects without relying solely on formal authority. Practical understanding of AI agent architecture, including tool usage, workflow orchestration, state or memory management, evaluation, guardrails, and agent reliability. Experience with multi-tenant SaaS platforms, infrastructure automation, distributed systems, or production-grade AI agents is particularly valuable. Familiarity with technologies such as AWS/EKS, Kubernetes, OpenTofu, FluxCD, Temporal, VictoriaMetrics, ClickHouse, Grafana, Go, or Rust is beneficial. Expertise across the entire stack is not required, provided you have strong fundamentals and can learn new technologies quickly. Ability to work effectively in a remote-first, asynchronous environment, taking ownership of technical decisions and collaborating with colleagues across locations. Benefits: Competitive compensation: Market-benchmarked base salary, performance-based variable pay, and impact-driven equity for most roles. Comprehensive health coverage: Health, dental, vision, and mental health benefits from day one, alongside flexible health stipends tailored to the relevant country. Flexible time off: Flexible vacation arrangements and modern leave policies designed to support personal well-being and work-life balance. Remote-first flexibility: Work remotely with a globally distributed team spanning more than 15 countries, within a high-trust, asynchronous working environment. AI-native culture: Work in an environment where AI is integrated into everyday engineering practices, tools, and decision-making. Accelerated professional growth: Develop expertise through complex technical challenges, cutting-edge technologies, and large-scale implementations. Technical ownership: Opportunities to influence infrastructure architecture, shape engineering practices, and build systems that support the next generation of AI applications. Collaborative global team: Learn from experienced professionals, share knowledge, and contribute to a culture that values technical excellence, inclusion, and continuous improvement. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1

Staff Software Engineer, AI/ML, ML Infrastructure, Search Ads

From $207/yr

In most instances, this position requires in-person interviews as part of the hiring process. Minimum qualifications: * Bachelor’s degree or equivalent practical experience. * 8 years of experience programming in Python or C++. * 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). * 5 years of experience testing, and launching software products. * 3 years of experience with software design and architecture. * 2 years of experience with GenAI techniques (e.g., large language models, multi-modal, large vision models) or with GenAI-related concepts (e.g., language modeling, computer vision). Preferred qualifications: * Master’s degree or PhD in Engineering, Computer Science, or a related technical field. * 8 years of experience with data structures and algorithms. * 3 years of experience in a technical leadership role leading project teams and setting technical direction. * 3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects. About The Job Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business. Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Responsibilities Learn more about benefits at Google . * Work across many different aspects of machine learning infrastructure and modeling. * Support new modeling techniques and products for changing infrastructure (training and serving). Prototype new infrastructure, automation, workflows, and tooling. * Analyze and debug performance, and make improvements. Improve robustness, reliability, usability of infrastructure. * Collaborate closely with other members of our team and partners to build systems that take advantage of and integrate seamlessly across our ecosystem. Engage with modeling teams to explore pain points and novel use cases. * Research emerging technology and make trade-off decisions on immediate wins vs longer-term stability. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .