Data Warehouse for ML/AI
Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI.
Our distributed data engine Daft is open-sourced and runs on 800k CPU cores daily. This is more compute than Frontier, the world's largest supercomputer!
Daft is used at leading AI/ML companies such as Amazon, TogetherAI, EssentialAI, CloudKitchens and more. It makes ML/AI workloads easy and performant to run alongside traditional relational tabular workloads.
Today's “Big Data” data tooling (Spark, Trino, Snowflake) was built for a world of tabular data analytics. They do not generalize well to the needs of modern ML/AI data workloads. We built Daft to be the successor to these Big Data technologies along these core principles:
Python-native: Python is the native language of ML/AI and most of data engineering today
First-Class Local Development UX: Interactive development in a local Python notebook or script is where the magic happens
Multimodal Data Support : Modern workloads require support for operations on complex types such as long-form text, images, tensors and more
Heterogenous Compute (GPUs): GPUs are a requirement for workloads that perform model batch inference as part of the overall query
Role Overview
As our Business Operations Manager , you will ensure the smooth running of day-to-day operations while tackling a variety of ad hoc projects—from setting up budgets and managing vendor relationships to establishing operations for new initiatives. This role calls for a proactive, problem-solving mindset and the ability to collaborate cross-functionally with Product, Engineering, Sales, and Finance.
If you’re excited to drive our day-to-day success and tackle diverse operational challenges in a rapidly scaling AI and data-focused environment, we’d love to hear from you!
Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI.
Our distributed data engine Daft is open-sourced and runs on 800k CPU cores daily. This is more compute than Frontier, the world's largest supercomputer!
Today's data tooling (Spark, Presto, Snowflake) was built for a world of tabular data analytics, but does not generalize to the needs of modern ML/AI such as multimodal data, heterogenous compute and user-defined Python algorithms.
Eventual and Daft bridge that gap, making ML/AI workloads easy to run alongside traditional tabular workloads.
We are well funded by investors such as YCombinator, Caffeinated Capital, Array.vc and top angels in the valley from Databricks, Meta and Lyft.
Our team has deep expertise in high performance computing, big data technologies, cloud infrastructure and machine learning. Our team members have previously worked in top technology companies such as AnyScale, Tesla and Lyft.
We are looking for exceptional individuals with a passion for technology and a strong sense of intellectual curiosity.
If that sounds like you, please reach out even if you don't see a specific role listed that matches your skillsets - we'd love to chat!