Open-Source Product Growth Engineer at Eventual (W22)
$120K - $140K  •  0.10% - 1.00%
Data Warehouse for ML/AI
San Francisco, CA
Any (new grads ok)
About Eventual

Eventual is a data platform that helps data scientists and engineers build data applications across ETL, analytics and ML/AI.

Our product is open-source and used at enterprise scale

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're growing - come grow with us!

We are well funded by investors such as YCombinator, Caffeinated Capital, 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!

About the role
Skills: CSS, Data Analytics

Key Responsibilities

As a Growth Engineer, you will be an early member of the Eventual team with primary responsibilities around making the Daft open-source product successful:

  • Daft user experience: building out features in Daft for observability, useability and performance
  • Technical partnerships: guide Daft development by partnering with users and other companies
  • Technical marketing/DevRel: building out a reproducible process for growing Daft usage and community through technical use-case demos and benchmarks

Our goal is to build the world’s best open-source distributed query engine, and your work will play a key role in realizing that vision.

We are a young startup - so be prepared to wear many hats such as tinkering with infrastructure, talking to customers and participating heavily in the core design process of our product!

What we look for

Our ideal candidate has the following characteristics:

  1. Proficient at technical writing with a passion for building compelling technical demos and tutorials
  2. Obsessive over documentation and user experience
  3. Familiarity with cloud data technologies such as AWS S3, Apache Iceberg and data warehousing

Nice to haves include:

  1. Proficiency with frontend development and design
  2. Experience with data science and data engineering

Most importantly, we are looking for someone who works well in small, focused teams with fast iterations and lots of autonomy. If you are passionate, intellectually curious and excited to build the next generation of distributed data technologies, we want you on the team!

Benefits and Remote Work

We are believers in both having the flexibility of remote work but also the importance of in-person work, especially at the earliest stages of a startup. We have a flexible hybrid approach to in-person work with at least 3 days of in-person work typically from Monday - Wednesday at our office in San Francisco.

We believe in providing employees with best-in-class compensation and benefits including meal allowances, comprehensive health coverage including medical, dental, vision and more.


Our stack is a mix of Python and Rust. We integrate closely with distributed systems such as Ray and storage engines such as Apache Iceberg and AWS S3.

Examples of domains that we work in include:

  • Databases: Daft employs modern query planning and optimizations for efficient execution of distributed workloads
  • Enterprise Big Data: Daft integrates with data lake technologies such as Apache Iceberg and Hive, with record-setting I/O throughputs to these storage formats
  • Low-Level System Programming: The execution core of Daft is written in Rust for memory-efficient data processing
Interview Process

Introductory Call [15m]

A short phone screen over video call with one of our co-founders for us to get acquainted, understand your aspirations and evaluate if there is a good fit in terms of the type of role you are looking for.

Technical Phone Screen [45m]

A technical phone screen question over video call to understand your technical abilities.

Technical Interview Panel [3.5hr]

Technical interviews with the rest of the Eventual team with questions to further understand your technical strengths, weaknesses and experiences.

Meet the Team

As many chats as necessary to get to know us - come have a coffee with our co-founders and existing team members to understand who we are and our goals, motivations and ambitions.

We look forward to meeting you!

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