Member of Engineering (Agent Sandboxes)

Remote (EMEA/East Coast)

ABOUT POOLSIDE

In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers.

Poolside exists to be this company: to build a world where AI will be the engine behind economically valuable work and scientific progress. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them.

ABOUT OUR TEAM

We were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year.

Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together. We’re in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we’ve assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development.

ABOUT THE ROLE

You will be part of our Platform engineering team focusing on our sandboxed agent execution environment to accelerate experimentation and foundation model training. In service of our evaluation and training teams, you will design, develop and operate services and systems that enable running agent sessions in a rich and safe environment at scale: 100s of thousands to millions of environments. You will also explore and evaluate different isolation techniques. You will also improve the reliability and performance of the systems behind the execution environment.

For more technical details, you can see some of our previous related works in this blog post.

We're prioritizing candidates with hands-on experience with sandbox techniques and container runtimes. Besides the technical background, we value candidates who can balance between speed and quality, who are self-driven, are willing to reprioritize works based on evolving project needs, and can distinguish between high impact tasks and trivial optimizations.

YOUR MISSION

Deliver a secure, reliable and performant sandboxed Agent execution environment to meet both the research and product needs at poolside.

RESPONSIBILITIES

Design and develop a sandboxed execution environment to facilitate Agent research and product development at poolside.
Own the complete development cycle of new features and capabilities in the Agent execution environment.
Collaborate with other teams to discuss, guide and meet their needs in the sandbox and code execution environment.
Continuously improve reliability and performance of the Agent execution environment.

SKILLS & EXPERIENCE

Strong programming skills, especially in Go, Python, or other systems programming languages.
Comfortable with distributed systems. Or have a good reason to be uncomfortable with them. We'd love to hear your battle scars.
Experience in Kubernetes or similar container/vm orchestration systems. Familiarity with public clouds like AWS, GCP, etc.
Experience in designing and building distributed systems.
Experience in virtualization or container runtime.
Experience in building large scale CI/CD platforms.
Plus: Experience building systems to support large scale machine learning workloads