karamazov

The most counterintuitive thing we found doing this is how often jobs (especially old jobs) are undersized. You can run faster and cheaper if you're running out of resources (especially memory).

Espresso AI | Staff ML, Staff Infra, FDE | Brooklyn or San Francisco | Full time

We're using LLMs to build neural optimizers, neural scheduling systems, and neural workload tuners. (If you're ex-Google, you can think of it like Borg powered by LLMs.)

Today we use ML to make data warehouses and spark jobs more efficient. We're hiring staff ML engineers to train models that can understand how much compute a job needs, how it scales to larger machines, whether a machine can run more jobs, and so on; and staff infra engineers to take those models and deploy them on real-world production systems.

We're also looking for FDEs who can help us talk to users and run pilots. This is a pretty technical role (you need to be able to do data analysis and debug in prod) that's also user-facing - it should be a good fit for a former (or future) technical founder.

If this sounds cool, please email me: ben [at] espresso [dot] ai

Espresso AI | Staff ML & Staff Infra Engineers | Brooklyn or San Francisco | Full time

We're using LLMs to build neural optimizers, neural scheduling systems, and neural workload tuners. (If you're ex-Google, you can think of it like Borg powered by LLMs.)

Today we use ML to make data warehouses and spark jobs more efficient. We're hiring staff ML engineers to train models that can understand how much compute a job needs, how it scales to larger machines, whether a machine can run more jobs, and so on; and staff infra engineers to take those models and deploy them on real-world production systems.

If this sounds cool, please email me: ben [at] espresso [dot] ai

Espresso AI | Staff Engineers | NYC ONSITE | Full Tim

We use ML to make data warehouses and spark jobs more efficient. We're hiring staff ML engineers to train models that can understand how much compute a job needs, how it scales to larger machines, whether a machine can run more jobs, and so on; and staff infra engineers to take those models and deploy them on real-world production systems.

If this sounds cool, please email me an intro and a resume: ben [at] espresso [dot] ai