Member of Engineering (Evaluations / Engineering)
AI summary of the role
Design and implement a scalable self-serve evaluation platform for frontier AI models.
What you’ll do
- Design a Python framework that makes it easy for poolsiders to implement both internal and public benchmarks in a centralized way
- Build and maintain the pipeline that runs distributed evaluations at scale
- Collaborate with modeling and product teams to identify opportunities to improve experimentation and evaluation tooling
What you’ll bring
- Strong engineering background
- Experience leading software projects cross functionally
- Experience building highly reliable and well tested services
- Experience with distributed systems
Technologies
Python · Kafka · Google pub/sub · GCP · AWS · Azure · Grafana · Prometheus · Datadog · distributed systems
About Poolside
AI coding assistant with foundation models (Malibu, Point) deployed on-premises in secure enterprise environments—targeting developers in defense, finance, and government sectors.
Series C
Source and classification
Internal deployment & tooling · Evidence for this classification:
are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year. Our R&D and production teams are a combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound our efforts. ABOUT THE ROLE Evaluation is one of the most important pillars of building a frontier model and product - it informs the direction of research and development, powers our experimentation and ensures quality and alignment with our users. To support this, we need a powerful pipeline and self-serve evaluation framework that helps poolsiders build, run and extract insight from evals
More from the job description
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. View GDPR Policy ABOUT OUR TEAM We are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year. Our R&D and production teams are a combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound o [... source excerpt omitted ...] our users. To support this, we need a powerful pipeline and self-serve evaluation framework that helps poolsiders build, run and extract insight from evals easily. In this role, you will design and implement this platform to build and run evaluations at scale. YOUR MISSION Build a scalable self-serve evaluation platform to power our research and development RESPONSIBILITIES Design a Python framework that makes it easy for poolsiders to implement both internal and public benchmarks in a centralized way Build and maintain the pipeline that runs distributed evaluations at scale Collaborate with modeling and product teams to identify opportunities to improve our experimentati
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