Staff Production Engineer Public Sector Jobs

At Carbon, our mission goes beyond 3D printing. We are enabling creators everywhere to make what the world needs, right now. Our company is built on the idea that diverse fields of study must come together to solve intractable problems. We have grand ambitions to create technology that will influence industry around the world. You will be joining a diverse team with a vibrant culture, where team success is as important as celebrating individual contributions. Help us change the world and we will help you change yours. Working at the intersection of hardware, software, and molecular science, Carbon delivers on the promise of 3D printing by helping commercial customers move beyond prototyping to produce real-world parts. Every one of those parts starts with a print job that flows through our cloud, so the infrastructure that runs it has to be fast, safe, and boringly reliable. We are looking for a Staff Infrastructure / DevOps Engineer to join a small, high-trust platform team that owns the foundations everything else at Carbon runs on: our AWS and GCP footprints, the networks that connect our cloud to printers in the field, the Terraform that describes all of it, and the CI/CD pipelines that ship software to production. You will be a core owner of that platform, drive substantial initiatives across it end to end, and raise the engineering bar for how we build and operate cloud infrastructure at Carbon. You will: Operate & Extend Multi-Cloud Infrastructure: Be a core owner of our AWS and GCP environments that host our Tier-0 services, keeping them resilient, cost-efficient, and secure while extending them to support the next generation of Carbon products. Build the Network Between Cloud and Printer: Design and maintain the networking that connects our cloud services to a global fleet of printers, including VPCs, peering, DNS, load balancing, TLS, and secure edge connectivity. You will make sure a printer in a customer's factory can reach the clo

About the job Own the test strategy that proves Graphcore’s next generation drivers are production-ready. As a Staff System Software Engineer, you will shape how Graphcore validates complex device drivers and platform tools. You will bring technical depth across Linux kernels, drivers and systems software. Your work will help raise the quality bar for products that support the next generation of AI compute. You will influence design decisions, guide test infrastructure and drive reliability across the stack. You will design, build, execute and report tests across component, system and acceptance levels. You will debug difficult issues using hardware and software tools, simulators and emulators. This is a hands-on technical leadership role with broad reach. You will help teams move quickly while protecting production quality. The team and culture The Drivers QA team verifies low-level kernel drivers, user-space libraries and platform tools. Work spans component tests, user acceptance and full system validation. You will work directly with driver developers, hardware, firmware, ML stack, Kubernetes and cloud management teams. Decisions are made close to the technical detail, with engineers expected to speak up and own outcomes. The team values engineers who think big, act fast and take responsibility. You will mentor others, raise standards and turn hard system problems into lasting improvements. What we're looking for · Deep experience validating Linux kernels, device drivers or similar systems software · Strong C++ and Python test development skills · Experience with CI/CD, build tools and modern automated testing practices · Ability to debug complex issues across hardware, firmware, drivers and upper software layers · Proven ownership of technical work, priorities and delivery in an agile engineering setting · Clear communication skills, including the ability to document and explain complex technical concepts While we have outlined a set of requirements, we value t

About the job Own the test strategy that proves Graphcore’s next generation drivers are production-ready. As a Staff System Software Engineer, you will shape how Graphcore validates complex device drivers and platform tools. You will bring technical depth across Linux kernels, drivers and systems software. Your work will help raise the quality bar for products that support the next generation of AI compute. You will influence design decisions, guide test infrastructure and drive reliability across the stack. You will design, build, execute and report tests across component, system and acceptance levels. You will debug difficult issues using hardware and software tools, simulators and emulators. This is a hands-on technical leadership role with broad reach. You will help teams move quickly while protecting production quality. The team and culture The Drivers QA team verifies low-level kernel drivers, user-space libraries and platform tools. Work spans component tests, user acceptance and full system validation. You will work directly with driver developers, hardware, firmware, ML stack, Kubernetes and cloud management teams. Decisions are made close to the technical detail, with engineers expected to speak up and own outcomes. The team values engineers who think big, act fast and take responsibility. You will mentor others, raise standards and turn hard system problems into lasting improvements. What we're looking for · Deep experience validating Linux kernels, device drivers or similar systems software · Strong C++ and Python test development skills · Experience with CI/CD, build tools and modern automated testing practices · Ability to debug complex issues across hardware, firmware, drivers and upper software layers · Proven ownership of technical work, priorities and delivery in an agile engineering setting · Clear communication skills, including the ability to document and explain complex technical concepts While we have outlined a set of requirements, we value t

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 Role Overview: We are seeking a highly skilled Staff AI Engineer – AI Platform to join our ClickUp Engineering team. In this role, you will play a critical part in both building the core AI platform and directly applying large language models (LLMs) to deliver intelligent features across ClickUp. You will focus on backend systems that enable scalable, reliable, and secure AI-powered capabilities, while also working hands-on with LLMs to solve real user problems and drive product innovation. Key Responsibilities: Architect, design, and implement scalable AI platform services that support the deployment, orchestration, and lifecycle management of LLMs and other AI models. Apply LLMs and other AI technologies directly to build and enhance ClickUp’s intelligent features, working closely with product and engineering teams to deliver impactful solutions. Build and maintain robust APIs and backend systems that enable seamless integration of AI-powered features into ClickUp’s core platform. Develop infrastructure for model serving, monitoring, logging, and automated evaluation to ensure high reliability and performance of AI services in production. Integrate with multiple LLM providers (e.g., OpenAI, Anthropic, Google) and manage model selection, routing, and fallback strategies for optimal performance and cost. Drive the adoption of best practices in AI privacy, security, and compliance, including data anonymization, secure data handling, and regulatory adherence. Optimize platform performance, scalability, and cost-efficiency, leveraging cloud-native technologies and distributed systems. Stay current with ad

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. What is our Mission Toast has established itself as the leading hospitality platform in the US, and we are now setting our sights on the global stage. As part of the International team, in our mission to expand globally, we are building what it takes to help our customers thrive. You will join a high-impact, high-ownership engineering team solving complex technical challenges. We are looking for leadership that balances technical excellence with a strong customer mindset to drive continuous growth for both the team and our platform. A day in the life (Responsibilities) Lead complex projects within a cross-functional collaborative and supportive environment. Design, build, deploy and maintain highly resilient and scalable systems critical to our customers’ success. Mentor and provide technical guidance to team members, leading technical discussions and promoting best engineering practices. Work directly with pilot customers (sometimes in the restaurant!) to ensure successful delivery and operation Advocate for best coding practices, raising the bar for yourself, your team, and the company. Leverage cutting-edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team. What you'll need to thrive (Requirements) 8+ years of experience in delivering high quality, reliable services to production using Java, Kotlin or other object oriented language(s) Proven track record of architecting, supporting and debugging large distributed & multi threaded applications. Drive to work across our tech stack and support other engineers in resolving challenging problems Passionate about making users happy and seeing people use your product in the wi

We are seeking a Staff Logistics Engineer to lead the development of the logistics function for a major vehicle program – inbound planning, internal material flow and warehousing, outbound transportation, and logistics engineering – from initial facility build-out through start of production. You will also serve as the senior logistics leader and primary liaison to a key international manufacturing partner supporting body shop and metallic component manufacturing, as well as material flow across vehicle assembly plants. This role reports to the Manager, Logistics Engineering. The ideal candidate has taken a logistics organization from a greenfield or brownfield site through start of production, has led new-product or new-program launches in a complex automotive manufacturing environment, and has managed an executive-level manufacturing partner relationship. You are comfortable being the single point of accountability for logistics on a major program and can travel as required. In this role, you will: Lead the end-to-end design, layout, and operational launch of new Logistics programs, including inbound planning, internal material flow and warehousing, outbound transportation, and logistics engineering (packaging, systems, and industrial engineering). Serve as the senior logistics lead and primary liaison to a key international manufacturing partner, representing Logistics in onsite workshops, program steering meetings, and executive alignment sessions. Lead Logistics readiness across the full site lifecycle – from greenfield/brownfield facility build-out and build-location decisions through new-product launch and start of production (SOP). Develop a comprehensive logistics launch plan to drive cross-functional logistics readiness for program gate reviews, surfacing schedule, capacity, and material-flow risks early and driving them to closure. Manage a r

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We believe Plaid has the power to be the next-gen Credit Bureau - supporting large scale adoption of cash flow into the credit underwriting process. The Credit Decisioning platform team is responsible for building best-in-class cashflow based insights products that enable lenders to make more holistic lending decisions and empower broader access to Credit products for prospective borrowers. We own the systems and tooling that form the platform to build and serve these insights at huge scale, partnering with our Data partners to release new products yearly. You will be defining the future architecture of Credit insights products and executing against an ambitious product roadmap. You will partner with our Product, Data Science, and Machine Learning team to iterate on and productionize new insights that enable our customers to make more holistic lending decisions. Responsibilities: Leading technical architecture and execution across credit insights products: everything from data fetching and online feature serving for API requests, to offline production pipelines and tooling for model training. Scaling and evolving the architecture through an expected ~100x increase in load from det

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we're behind some of Spotify's most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them. Surfaces Moments is a ML team within the Personalization Mission focused on creating moment-based experiences across Spotify surfaces. The team owns and evolves the experiences that help listeners quickly connect with the content that matters most to them, including the Home Shortcuts experience and the underlying intelligence that powers it. By combining cutting-edge machine learning, recommendation systems, and product thinking, the team delivers highly relevant, personalized experiences to millions of listeners around the world. As a Staff Machine Learning Engineer, you will help shape the future of personalized discovery and engagement at Spotify. You'll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences that delight users and drive meaningful impact. This role is ideal for someone who enjoys taking models from research to production, driving technical direction in ambiguous problem spaces, and solving complex personalization challenges at global scale. What You'll Do

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we're behind some of Spotify's most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them. Surfaces Moments is a ML team within the Personalization Mission focused on creating moment-based experiences across Spotify surfaces. The team owns and evolves the experiences that help listeners quickly connect with the content that matters most to them, including the Home Shortcuts experience and the underlying intelligence that powers it. By combining cutting-edge machine learning, recommendation systems, and product thinking, the team delivers highly relevant, personalized experiences to millions of listeners around the world. As a Staff Machine Learning Engineer, you will help shape the future of personalized discovery and engagement at Spotify. You'll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences that delight users and drive meaningful impact. This role is ideal for someone who enjoys taking models from research to production, driving technical direction in ambiguous problem spaces, and solving complex personalization challenges at global scale. What You'll Do <li style="font-size: 11pt; font-family: Arial, san

Who we are Lab37 Robotics , is a technology company focused on the development and deployment of robots designed specifically for direct-to-customer food production. Our mission is to revolutionize the food industry by creating innovative robotic solutions that enhance efficiency, quality, and customer satisfaction. We are passionate about pushing the boundaries of technology to deliver cutting-edge products that meet the evolving needs of our clients. About the Role Build the software platform that connects kitchen teams, cloud services, and robotic production systems. As a Staff Full-Stack Software Engineer, you will own significant product and technical areas from definition through production. You will work across frontend, backend, cloud infrastructure, integrations, and robot-adjacent interfaces. You will partner closely with product, culinary, operations, robotics, firmware and mechanical teams to turn ambiguous operational problems into reliable software. We are looking for someone who remains deeply hands-on while providing technical leadership like shaping architecture, raising engineering quality, mentoring others, and helping the Los Angeles team scale. What you'll do Own and deliver complex full-stack initiatives spanning user interfaces, backend services, cloud infrastructure, and external integrations. Lead technical design and architecture for new capabilities, balancing speed, reliability, maintainability, and operational simplicity. Partner directly with product, culinary, and operations teams to understand workflows, clarify requirements, and define pragmatic solutions. Build software for configuring, observing, and managing kitchen operations, including order intake, menus, recipes, tasks, alerts, and performance visibility. Integrate with delivery and commerce platforms to receive orders, synchronize menus, and communicate order state. Create and integrate operator-facing and robot-adjacent interfaces used in physical

About the job Build the test systems that help deliver AI hardware at production scale. As a Staff Hardware System Test Engineer, you'll develop manufacturing test solutions for complex compute systems. Your work will help ensure products reach customers with the quality and reliability expected from Graphcore. You'll shape how hardware is tested across Host Processor Modules, server blades, and multi-node racks. The tools, test content, and diagnostics you create will directly influence manufacturing efficiency and product quality. You'll develop test software, design test approaches, and solve challenging hardware issues. You'll work closely with hardware, firmware, software, and manufacturing teams to improve test coverage and diagnose problems quickly. This role offers broad technical scope across silicon, boards, systems, software, and manufacturing. You'll have the opportunity to influence engineering decisions and raise technical standards across the organisation. The team and culture The Product Test and Diagnosis team owns the strategy for finding and diagnosing hardware defects throughout the product lifecycle. The team works across silicon, boards, servers, and rack-scale systems. Day to day, engineers work closely with design, firmware, software, quality, and manufacturing teams. Problems are solved through data, technical debate, and hands-on investigation. You'll join a global engineering group spanning the UK, Taiwan, India, and the United States. People are trusted to take ownership, make decisions, and drive improvements that deliver measurable results. What we're looking for Essential: Significant experience developing and deploying manufacturing test solutions for high-performance servers, accelerators (GPU-class), or comparable large-scale compute hardware. Strong technical leadership as an individual contributor, with experience owning substantial technical workstreams and collab

Manufacturing Test Engineer – Server Hardware Graphcore is a globally recognised leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialised processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. We are opening a new AI Engineering Campus in Austin, which will play a central role in Graphcore's work building the future of AI computing. Role Overview We are seeking an experienced Manufacturing Test Engineer to support high-volume server manufacturing from board-level test through system-level production test. This role will work closely with an ODM manufacturing partner to define, implement, validate, and optimize the manufacturing test strategy for L6 board-level products , including ICT, MDA, and Board Functional Test , as well as support L10 system-level manufacturing test . The ideal candidate has strong experience in server hardware manufacturing, Linux-based test environments, diagnostic test coverage, fixture requirements, yield improvement, and root cause corrective action processes. This role requires both technical depth and hands-on manufacturing execution experience, with the ability to drive best practices across test development, factory readiness, quality planning, and ongoing production support. Key Responsibilities Manufacturing Test Strategy and Planning Work with ODM partners to define and execute the manufacturing test strategy for L6 board-level production . Develop and review test plans covering: In-Circuit Test, or ICT Manufacturing Defect Analyzer, or MDA Board Functional Test Diagnostic coverage requirements Manufacturing line test flow Fai

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We are the Data Foundation & AI team within Plaid’s Data organization. Our mission is to build the shared ML and AI infrastructure that powers intelligent capabilities across Plaid’s product suite. We develop the foundational systems, models, and data assets that transform Plaid’s unique financial network data into scalable, general-purpose representations that teams across the company can leverage. Our work spans the full ML lifecycle — from large-scale data curation and model pretraining to production serving, evaluation, and monitoring. As part of the team, you’ll work at the intersection of machine learning infrastructure, applied AI, and distributed systems, helping establish the core AI platform that enables innovation across Plaid. As a Staff Machine Learning Engineer, you will lead the technical strategy and development of Plaid’s foundation models, driving key decisions across pretraining objectives, model architecture, and fine-tuning approaches that power a wide range of downstream product applications. You will serve as the technical lead for the full machine learning lifecycle, overseeing everything from data curation and experimentation to production deployment,

The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, enabling customers to ship AI with confidence. As a Staff Engineer, you’ll lead the development of new features and foundational capabilities within Datadog’s LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production. Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Drive design and implementation of LLM observability features. Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit Develop and extend tools for tracing, evaluating, and debugging LLMs Influence architecture decisions and mentor engineers to build resilient, high-performance systems Stay close to customer pain points and use those insights to guide product and engineering priorities Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering or r

The Agent Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, enabling customers to ship AI with confidence. As a Staff Engineer, you’ll lead the development of new features and foundational capabilities within Datadog’s LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production. Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Drive design and implementation of LLM observability features. Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit Develop and extend tools for tracing, evaluating, and debugging LLMs Influence architecture decisions and mentor engineers to build resilient, high-performance systems Stay close to customer pain points and use those insights to guide product and engineering priorities Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering or related scie