Careers at Parasail

About Parasail Parasail is building the AI Supercloud, helping AI teams deploy and scale inference across a distributed network of GPUs. We make it easier for developers to bring AI applications into production with the performance, reliability, and flexibility they need. We’re looking for 2027 graduates who love building software, are curious about AI, and want to work directly with the people using what they build. As a Forward Deployed Engineer, you’ll help customers turn ideas into working applications—and use what you learn to make Parasail better. The Impact You’ll Have You’ll work at the intersection of software engineering, AI infrastructure, and customer problem-solving. Working alongside experienced engineers, you’ll help customers understand their requirements, build and test solutions, and bring real workloads into production. You’ll write code, investigate technical problems, and see firsthand how your work affects a customer’s product. You’ll receive guidance and mentorship while taking on increasing ownership of projects from initial discovery through deployment. Example projects may include: Customer integrations: Build integrations and example applications that connect customers’ products to Parasail’s inference platform. Prototypes and evaluations: Turn a customer’s use case into a working prototype, then evaluate model quality, latency, throughput, and cost against their needs. Production deployments: Help customers move from a successful experiment to a reliable deployment, working through issues such as concurrency, retries, streaming, and error handling. Performance investigations: Reproduce and diagnose issues across application code, API integrations, and inference workloads, partnering with engineering to resolve them. Reusable developer tools: Turn lessons from customer projects into examples, documentation, and tooling that help the next developer get started faster. You’ll also bring customer feedback into product and engineering discussions, helping the team identify improvements that benefit many users. What You’ll Bring Completing a bachelor’s or master’s degree in Computer Science, Engineering, or a related technical field in 2027, or equivalent practical experience. Experience building software through coursework, internships, research, open-source contributions, or personal projects. Proficiency in at least one programming language, such as Python, TypeScript, or Go. A foundation in software engineering fundamentals, including data structures, APIs, testing, and debugging. Clear written and verbal communication, with an interest in explaining technical ideas and understanding other people’s goals. Resourcefulness when facing an unfamiliar problem: you ask thoughtful questions, test assumptions, and follow through. Curiosity about AI models, inference, and what it takes to run AI applications in production. Enthusiasm for working directly with customers and collaborating in a fast-moving startup. We don’t expect you to arrive as an expert in AI infrastructure. We’re looking for strong fundamentals, evidence that you enjoy building, and a willingness to learn. Helpful, but Not Required Projects using LLMs, open-source models, or inference APIs. Familiarity with cloud infrastructure, Linux, containers, or distributed systems. Experience measuring application performance or evaluating model outputs. Experience helping others solve technical problems through teaching, tutoring, technical communities, or customer-facing work. Why Join Parasail? Build for real users. Work directly with developers and see your contributions become part of their applications. Learn across the stack. Develop practical experience in AI applications, inference infrastructure, and production engineering. Grow through ownership. Take on meaningful projects with support from experienced teammates. Help shape the product. Turn what you learn from customer deployments into improvements to Parasail’s platform and developer experience. If you’re excited to build, learn quickly, and help developers bring AI into production, we’d love to hear from you.

Engineering Site Lead, Europe AI companies need inference that’s fast, reliable, and economical at scale. Parasail delivers it. We’re building an enterprise-grade inference cloud for open-weight models where customers pay for the tokens they use, and we handle everything required to serve them. Behind one OpenAI-compatible API, we pool GPU capacity from providers around the world and continuously optimize where and how workloads run. That means turning a changing mix of hardware, networks, and infrastructure into a service customers can trust. We’ve raised a $32 million Series A, and we’re scaling beyond trillions of tokens a day. You’ll have the ownership and reach to shape how we get there. The Role We’re hiring an Engineering Site Lead to build and lead Parasail’s engineering presence in Europe. You’ll recruit a strong team, establish how it works, and take ownership of critical infrastructure behind our global inference cloud. This is a hands-on engineering leadership role. You’ll help set technical direction, contribute to architecture and implementation, and develop engineers who can own complex systems in production. Your initial focus will be infrastructure and reliability: making a changing fleet of GPUs, providers, and networks operate as one dependable service. You’ll work directly with engineering leadership and infrastructure, platform, and inference engineers across regions. You’ll give the European team clear ownership, connect its work to company priorities, and make collaboration across time zones effective. What You’ll Do Build the European engineering team. Recruit, hire, and develop engineers with complementary strengths in infrastructure, distributed systems, and production reliability. Set direction and deliver. Translate company priorities into a focused roadmap, make sound technical tradeoffs, and help the team carry work from design through production. Stay close to the engineering. Contribute to architecture, review designs and code, and work directly on the most important technical problems. Scale a global GPU fleet. Lead improvements to the Kubernetes infrastructure behind provisioning, networking, storage, and deployment across providers and regions. Make reliability a team responsibility. Build better isolation, failover, observability, and recovery. Establish sustainable incident response and turn production lessons into stronger systems. Build software that runs infrastructure. Help the team automate capacity expansion, deployments, and maintenance, reducing manual work and making changes safer. Connect teams across regions. Establish clear ownership, useful documentation, and effective handoffs so teams can make progress across time zones. Shape the engineering culture. Set a high bar for technical quality, accountability, and collaboration while giving engineers room to make decisions and grow. What You Bring Experience leading engineering teams responsible for production infrastructure or distributed systems. A track record of hiring and developing strong engineers, setting priorities, and delivering meaningful technical outcomes. The technical depth to guide architecture, challenge assumptions, and contribute directly when needed. Experience building and operating reliable services, with real ownership of their performance in production. Strong Linux fundamentals and practical knowledge of networking, storage, containers, and Kubernetes. The ability to write maintainable software and automation to solve infrastructure problems. A systematic approach to debugging problems that cross application, cluster, network, and hardware boundaries. Good judgment about when to move quickly, when to simplify, and where reliability matters most. Clear communication and experience working effectively across teams and time zones. You may be an engineering manager who remains close to the technology, a technical lead with experience building teams, or an infrastructure leader ready to establish a new site. What matters is your ability to build a strong team and take responsibility for the systems it delivers. Nice to Have Experience establishing or growing an engineering office or regional team. Experience with multi-region, multi-provider, or bare-metal infrastructure. Familiarity with GPUs, model serving, or inference systems such as vLLM or SGLang. Experience building highly available services, multi-tenant platforms, or distributed data systems. Experience leading teams through rapid growth while maintaining technical quality and sustainable operations. Why Join Parasail You’ll shape both a team and the systems it owns. As our Engineering Site Lead in Europe, you’ll influence who we hire, how we work, and how reliably and efficiently customers can run AI in production. This is a small team tackling problems at substantial scale. You’ll work close to the hardware, deep in distributed systems, and alongside engineers optimizing the inference stack. You’ll own meaningful architecture and organizational decisions and build a European engineering team with a direct role in Parasail’s growth.

Engineering Site Lead, Europe AI companies need inference that’s fast, reliable, and economical at scale. Parasail delivers it. We’re building an enterprise-grade inference cloud for open-weight models where customers pay for the tokens they use, and we handle everything required to serve them. Behind one OpenAI-compatible API, we pool GPU capacity from providers around the world and continuously optimize where and how workloads run. That means turning a changing mix of hardware, networks, and infrastructure into a service customers can trust. We’ve raised a $32 million Series A, and we’re scaling beyond trillions of tokens a day. You’ll have the ownership and reach to shape how we get there. The Role We’re hiring an Engineering Site Lead to build and lead Parasail’s engineering presence in Europe. You’ll recruit a strong team, establish how it works, and take ownership of critical infrastructure behind our global inference cloud. This is a hands-on engineering leadership role. You’ll help set technical direction, contribute to architecture and implementation, and develop engineers who can own complex systems in production. Your initial focus will be infrastructure and reliability: making a changing fleet of GPUs, providers, and networks operate as one dependable service. You’ll work directly with engineering leadership and infrastructure, platform, and inference engineers across regions. You’ll give the European team clear ownership, connect its work to company priorities, and make collaboration across time zones effective. What You’ll Do Build the European engineering team. Recruit, hire, and develop engineers with complementary strengths in infrastructure, distributed systems, and production reliability. Set direction and deliver. Translate company priorities into a focused roadmap, make sound technical tradeoffs, and help the team carry work from design through production. Stay close to the engineering. Contribute to architecture, review designs and code, and work directly on the most important technical problems. Scale a global GPU fleet. Lead improvements to the Kubernetes infrastructure behind provisioning, networking, storage, and deployment across providers and regions. Make reliability a team responsibility. Build better isolation, failover, observability, and recovery. Establish sustainable incident response and turn production lessons into stronger systems. Build software that runs infrastructure. Help the team automate capacity expansion, deployments, and maintenance, reducing manual work and making changes safer. Connect teams across regions. Establish clear ownership, useful documentation, and effective handoffs so teams can make progress across time zones. Shape the engineering culture. Set a high bar for technical quality, accountability, and collaboration while giving engineers room to make decisions and grow. What You Bring Experience leading engineering teams responsible for production infrastructure or distributed systems. A track record of hiring and developing strong engineers, setting priorities, and delivering meaningful technical outcomes. The technical depth to guide architecture, challenge assumptions, and contribute directly when needed. Experience building and operating reliable services, with real ownership of their performance in production. Strong Linux fundamentals and practical knowledge of networking, storage, containers, and Kubernetes. The ability to write maintainable software and automation to solve infrastructure problems. A systematic approach to debugging problems that cross application, cluster, network, and hardware boundaries. Good judgment about when to move quickly, when to simplify, and where reliability matters most. Clear communication and experience working effectively across teams and time zones. You may be an engineering manager who remains close to the technology, a technical lead with experience building teams, or an infrastructure leader ready to establish a new site. What matters is your ability to build a strong team and take responsibility for the systems it delivers. Nice to Have Experience establishing or growing an engineering office or regional team. Experience with multi-region, multi-provider, or bare-metal infrastructure. Familiarity with GPUs, model serving, or inference systems such as vLLM or SGLang. Experience building highly available services, multi-tenant platforms, or distributed data systems. Experience leading teams through rapid growth while maintaining technical quality and sustainable operations. Why Join Parasail You’ll shape both a team and the systems it owns. As our Engineering Site Lead in Europe, you’ll influence who we hire, how we work, and how reliably and efficiently customers can run AI in production. This is a small team tackling problems at substantial scale. You’ll work close to the hardware, deep in distributed systems, and alongside engineers optimizing the inference stack. You’ll own meaningful architecture and organizational decisions and build a European engineering team with a direct role in Parasail’s growth.

AI companies need inference that’s fast, reliable, and economical at scale. Parasail delivers it. We’re building an enterprise-grade inference cloud for open-weight models where customers pay for the tokens they use, and we handle everything required to serve them. Behind one OpenAI-compatible API, we pool GPU capacity from providers around the world and continuously optimize where and how workloads run. That means turning a changing mix of hardware, networks, and infrastructure into a service customers can trust. We’ve raised a $32 million Series A, and we’re scaling beyond trillions of tokens a day. You’ll have the ownership and reach to shape how we get there. The Role At Parasail, reliability is an engineering problem that spans the entire stack. A GPU fails. A provider goes down. Traffic spikes. Customers still expect their inference to work. We’re hiring Site Reliability Engineers to build the systems that make that possible. You’ll own infrastructure across our global GPU fleet, write software that automates operations, and make the platform better at detecting, surviving, and recovering from failures. You’ll work directly with infrastructure, platform, and inference engineers in a flat organization. We welcome SREs, software engineers, platform engineers, and systems engineers who want to build ambitious systems and take responsibility for how they perform in production. What You’ll Do Scale a global GPU fleet. Build and improve the Kubernetes infrastructure behind provisioning, networking, storage, and service deployment across providers and regions. Make failure survivable. Design better isolation, failover, and recovery so hardware and infrastructure failures have less impact on customers. Build software that runs infrastructure. Automate capacity expansion, deployments, and maintenance, eliminating manual work and making changes safer. Make the system understandable. Develop observability and diagnostics that reveal bottlenecks, surface failures, and help engineers act quickly. Own the production feedback loop. Respond to incidents, get to the root cause, and turn what you learn into stronger systems. Push the platform forward. Work across the stack to improve performance, utilization, security, and reliability as inference demand grows. What You Bring Experience building and operating production infrastructure or distributed systems, with real ownership of reliability. Strong Linux fundamentals and practical knowledge of networking, storage, and containers. Hands-on experience running Kubernetes in production. The ability to write maintainable software and automation to solve infrastructure problems. A systematic approach to debugging problems that cross application, cluster, network, and hardware boundaries. Good judgment about when to move quickly, when to simplify, and where reliability matters most. The initiative to take a problem from investigation through implementation and work closely with teammates along the way. Your strongest skill might be software development, distributed systems, or infrastructure operations. We’re building a team with complementary strengths; your previous job title matters less than what you can build and own. Nice to Have Experience with multi-region, multi-provider, or bare-metal infrastructure. Familiarity with GPUs, model serving, or inference systems such as vLLM or SGLang. Experience with infrastructure as code, CI/CD, observability, or automated recovery. Experience building highly available services, multi-tenant platforms, or distributed data systems. Why Join Parasail The systems you build will determine how reliably and efficiently customers can run AI in production. You’ll work close to the hardware, deep in distributed systems, and alongside engineers optimizing the inference stack. This is a small team tackling problems at substantial scale. You’ll own meaningful architecture decisions, ship improvements directly into production, and help build the foundation for the next stage of AI infrastructure.

AI companies need inference that’s fast, reliable, and economical at scale. Parasail delivers it. We’re building an enterprise-grade inference cloud for open-weight models where customers pay for the tokens they use, and we handle everything required to serve them. Behind one OpenAI-compatible API, we pool GPU capacity from providers around the world and continuously optimize where and how workloads run. That means turning a changing mix of hardware, networks, and infrastructure into a service customers can trust. We’ve raised a $32 million Series A, and we’re scaling beyond trillions of tokens a day. You’ll have the ownership and reach to shape how we get there. The Role At Parasail, reliability is an engineering problem that spans the entire stack. A GPU fails. A provider goes down. Traffic spikes. Customers still expect their inference to work. We’re hiring Site Reliability Engineers to build the systems that make that possible. You’ll own infrastructure across our global GPU fleet, write software that automates operations, and make the platform better at detecting, surviving, and recovering from failures. You’ll work directly with infrastructure, platform, and inference engineers in a flat organization. We welcome SREs, software engineers, platform engineers, and systems engineers who want to build ambitious systems and take responsibility for how they perform in production. What You’ll Do Scale a global GPU fleet. Build and improve the Kubernetes infrastructure behind provisioning, networking, storage, and service deployment across providers and regions. Make failure survivable. Design better isolation, failover, and recovery so hardware and infrastructure failures have less impact on customers. Build software that runs infrastructure. Automate capacity expansion, deployments, and maintenance, eliminating manual work and making changes safer. Make the system understandable. Develop observability and diagnostics that reveal bottlenecks, surface failures, and help engineers act quickly. Own the production feedback loop. Respond to incidents, get to the root cause, and turn what you learn into stronger systems. Push the platform forward. Work across the stack to improve performance, utilization, security, and reliability as inference demand grows. What You Bring Experience building and operating production infrastructure or distributed systems, with real ownership of reliability. Strong Linux fundamentals and practical knowledge of networking, storage, and containers. Hands-on experience running Kubernetes in production. The ability to write maintainable software and automation to solve infrastructure problems. A systematic approach to debugging problems that cross application, cluster, network, and hardware boundaries. Good judgment about when to move quickly, when to simplify, and where reliability matters most. The initiative to take a problem from investigation through implementation and work closely with teammates along the way. Your strongest skill might be software development, distributed systems, or infrastructure operations. We’re building a team with complementary strengths; your previous job title matters less than what you can build and own. Nice to Have Experience with multi-region, multi-provider, or bare-metal infrastructure. Familiarity with GPUs, model serving, or inference systems such as vLLM or SGLang. Experience with infrastructure as code, CI/CD, observability, or automated recovery. Experience building highly available services, multi-tenant platforms, or distributed data systems. Why Join Parasail The systems you build will determine how reliably and efficiently customers can run AI in production. You’ll work close to the hardware, deep in distributed systems, and alongside engineers optimizing the inference stack. This is a small team tackling problems at substantial scale. You’ll own meaningful architecture decisions, ship improvements directly into production, and help build the foundation for the next stage of AI infrastructure.

AI companies need inference that’s fast, reliable, and economical at scale. Parasail delivers it. We’re building an enterprise-grade inference cloud for open-weight models where customers pay for the tokens they use, and we handle everything required to serve them. Behind one OpenAI-compatible API, we pool GPU capacity from providers around the world and continuously optimize where and how workloads run. That means turning a changing mix of hardware, networks, and infrastructure into a service customers can trust. We’ve raised a $32 million Series A, and we’re scaling beyond trillions of tokens a day. You’ll have the ownership and reach to shape how we get there. The Role At Parasail, reliability is an engineering problem that spans the entire stack. A GPU fails. A provider goes down. Traffic spikes. Customers still expect their inference to work. We’re hiring Site Reliability Engineers to build the systems that make that possible. You’ll own infrastructure across our global GPU fleet, write software that automates operations, and make the platform better at detecting, surviving, and recovering from failures. You’ll work directly with infrastructure, platform, and inference engineers in a flat organization. We welcome SREs, software engineers, platform engineers, and systems engineers who want to build ambitious systems and take responsibility for how they perform in production. What You’ll Do Scale a global GPU fleet. Build and improve the Kubernetes infrastructure behind provisioning, networking, storage, and service deployment across providers and regions. Make failure survivable. Design better isolation, failover, and recovery so hardware and infrastructure failures have less impact on customers. Build software that runs infrastructure. Automate capacity expansion, deployments, and maintenance, eliminating manual work and making changes safer. Make the system understandable. Develop observability and diagnostics that reveal bottlenecks, surface failures, and help engineers act quickly. Own the production feedback loop. Respond to incidents, get to the root cause, and turn what you learn into stronger systems. Push the platform forward. Work across the stack to improve performance, utilization, security, and reliability as inference demand grows. What You Bring Experience building and operating production infrastructure or distributed systems, with real ownership of reliability. Strong Linux fundamentals and practical knowledge of networking, storage, and containers. Hands-on experience running Kubernetes in production. The ability to write maintainable software and automation to solve infrastructure problems. A systematic approach to debugging problems that cross application, cluster, network, and hardware boundaries. Good judgment about when to move quickly, when to simplify, and where reliability matters most. The initiative to take a problem from investigation through implementation and work closely with teammates along the way. Your strongest skill might be software development, distributed systems, or infrastructure operations. We’re building a team with complementary strengths; your previous job title matters less than what you can build and own. Nice to Have Experience with multi-region, multi-provider, or bare-metal infrastructure. Familiarity with GPUs, model serving, or inference systems such as vLLM or SGLang. Experience with infrastructure as code, CI/CD, observability, or automated recovery. Experience building highly available services, multi-tenant platforms, or distributed data systems. Why Join Parasail The systems you build will determine how reliably and efficiently customers can run AI in production. You’ll work close to the hardware, deep in distributed systems, and alongside engineers optimizing the inference stack. This is a small team tackling problems at substantial scale. You’ll own meaningful architecture decisions, ship improvements directly into production, and help build the foundation for the next stage of AI infrastructure.

AI companies need inference that’s fast, reliable, and economical at scale. Parasail delivers it. We’re building an enterprise-grade inference cloud for open-weight models where customers pay for the tokens they use, and we handle everything required to serve them. Behind one OpenAI-compatible API, we pool GPU capacity from providers around the world and continuously optimize where and how workloads run. That means turning a changing mix of hardware, networks, and infrastructure into a service customers can trust. We’ve raised a $32 million Series A, and we’re scaling beyond trillions of tokens a day. You’ll have the ownership and reach to shape how we get there. The Role At Parasail, reliability is an engineering problem that spans the entire stack. A GPU fails. A provider goes down. Traffic spikes. Customers still expect their inference to work. We’re hiring Site Reliability Engineers to build the systems that make that possible. You’ll own infrastructure across our global GPU fleet, write software that automates operations, and make the platform better at detecting, surviving, and recovering from failures. You’ll work directly with infrastructure, platform, and inference engineers in a flat organization. We welcome SREs, software engineers, platform engineers, and systems engineers who want to build ambitious systems and take responsibility for how they perform in production. What You’ll Do Scale a global GPU fleet. Build and improve the Kubernetes infrastructure behind provisioning, networking, storage, and service deployment across providers and regions. Make failure survivable. Design better isolation, failover, and recovery so hardware and infrastructure failures have less impact on customers. Build software that runs infrastructure. Automate capacity expansion, deployments, and maintenance, eliminating manual work and making changes safer. Make the system understandable. Develop observability and diagnostics that reveal bottlenecks, surface failures, and help engineers act quickly. Own the production feedback loop. Respond to incidents, get to the root cause, and turn what you learn into stronger systems. Push the platform forward. Work across the stack to improve performance, utilization, security, and reliability as inference demand grows. What You Bring Experience building and operating production infrastructure or distributed systems, with real ownership of reliability. Strong Linux fundamentals and practical knowledge of networking, storage, and containers. Hands-on experience running Kubernetes in production. The ability to write maintainable software and automation to solve infrastructure problems. A systematic approach to debugging problems that cross application, cluster, network, and hardware boundaries. Good judgment about when to move quickly, when to simplify, and where reliability matters most. The initiative to take a problem from investigation through implementation and work closely with teammates along the way. Your strongest skill might be software development, distributed systems, or infrastructure operations. We’re building a team with complementary strengths; your previous job title matters less than what you can build and own. Nice to Have Experience with multi-region, multi-provider, or bare-metal infrastructure. Familiarity with GPUs, model serving, or inference systems such as vLLM or SGLang. Experience with infrastructure as code, CI/CD, observability, or automated recovery. Experience building highly available services, multi-tenant platforms, or distributed data systems. Why Join Parasail The systems you build will determine how reliably and efficiently customers can run AI in production. You’ll work close to the hardware, deep in distributed systems, and alongside engineers optimizing the inference stack. This is a small team tackling problems at substantial scale. You’ll own meaningful architecture decisions, ship improvements directly into production, and help build the foundation for the next stage of AI infrastructure.