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Roles
Compensation
Full time (no salary details provided).
- Salary period
- unclear
Tech stack
Required
Location
Brooklyn, San Francisco
Work setup
- Employment
- full-time
- Level
- Senior
- Remote policy
- Onsite (Brooklyn or San Francisco listed; no remote details provided).
- Remote scope
- onsite
Role details
Responsibilities
- Train models to understand compute requirements of jobs
- Train models to predict how jobs scale to larger machines
- Train models to determine whether a machine can run more jobs
- Deploy trained models on real-world production systems
- Help talk to users and run pilots
- Perform data analysis and debug in production
Requirements
- Ability to do data analysis
- Ability to debug in production
Application
Please email me at alexis [at] espresso [dot] ai.
- Portfolio
- unclear
- GitHub
- unclear
- Cover letter
- unclear
- Apply flow
Company context
- Product
- Neural optimizers, neural scheduling systems, neural workload tuners for making data warehouses and Spark jobs more efficient
- Industry
- unclear
Contact
Alexis
alexis [at] espresso [dot] ai
Description
We’re using LLMs to build neural optimizers, neural scheduling systems, and neural workload tuners. 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.
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