---
title: 'Software Engineer, Data Systems at Eventual'
canonical: 'https://feeny.ai/job/software-engineer-data-systems-eventual-san-francisco-je7sws8z9p6d'
type: 'job'
last_seen: '2026-09-09'
---

# Software Engineer, Data Systems at Eventual

- **Company:** Eventual
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2026-09-03
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/eventualcomputing/ff541fc0-b72e-40ad-84dc-6baa22570356

## Job description

## ABOUT EVENTUAL

From humanoid robots to autonomous vehicles, every Physical AI model is trained on petabytes of video, lidar, radar, and sensor data. Today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not video corpora. And understanding that video still means paying a person to watch it, ten dollars an hour of footage at the low end. So teams check a sample and hope it represents the rest. The footage grows every year; the budget to look at it doesn't.

Eventual was founded in 2022 to close that gap. Our open-source engine, Daft http://daft, is purpose-built for multimodal AI: 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at companies like Mobileye, TogetherAI. On top of it we're building the infrastructure that finds any situation you can describe across a fleet's entire video history, and turns it into a training set or an alert someone can still act on. We fine-tune and run the vision models ourselves, which makes indexing every hour cheaper than annotating a sample.

We're building this with the top Physical AI labs and GPU cloud providers. We've raised $30M from investors like Felicis, CRV, Y Combinator, and angels from the co-founders of Databricks and Perplexity. Our team comes from AWS, Lyft, and Tesla. We powered the last generation of Physical AI in self-driving; now we're doing it for the next.

Join our small (but powerful!) team, 4 days/week in our SF Mission District office.

## OUR MISSION:

Our goal is to build Scenario Mining and Data Curation for robot fleet data. We empower Physical AI and robotics teams to instantly find, curate, and stream the data they need to train frontier models.

Eventual is an agile team where every engineer has high ownership across the stack from our compute infrastructure, to our data storage/querying layers and model training/deployment.

## YOUR ROLE:

As a Software Engineer on the Data Systems team, you will build key capabilities for Eventual. We build storage and a high-throughput data engine over petabytes of video, lidar and high-frequency telemetry data to power frontier robotics and Physical AI labs. You will work directly on the architecture powering real-time indexing of perception/robotics data, distributed storage/compute, and dataloading at line-rate to GPUs for model training and inference. We operate as a tight-knit, experienced engineering team that values technical autonomy, deep execution, and a passion for solving hard distributed systems problems.

## KEY RESPONSIBILITIES:

- Multimodal Storage (Data Lake): built against modern columnar data lake formats (Apache Parquet, Apache Iceberg etc) optimized for high-dimensional video, lidar and sensor logs.
- Query Engine: build powerful querying capabilities. Our multi-stage query systems are built on database fundamentals such as partitioning, indexing, query planning, embeddings/vector search for search and retrieval as well as LLMs/VLMs for perception-based query predicates.
- Dataloading: Improve memory stability, throughput, and zero-copy data flow through streaming computation and line-rate CUDA tensor delivery to GPUs.

## WHAT WE LOOK FOR:

- Proven track record building resilient, high-throughput distributed systems or database engines using Rust or C++.
- 3+ years of experience diving deep into engine internals—such as vectorized execution, query planning/optimization, distributed task scheduling, or zero-copy networking.
- Practical exposure to scaling cloud infrastructure (AWS S3) and managing heavy-compute data pipelines (bonus points for experience with CUDA, GPU streaming, or video decoding frameworks).
- High agency and adaptability to thrive in an autonomous, fast-paced startup environment building cutting-edge infrastructure for frontier robotics

## PERKS & BENEFITS

- In-person tight knit team with 4x a week in office
- Competitive comp and startup equity
- Catered lunches and dinners for SF employees
- Commuter benefit
- Team building events & poker nights
- Health, vision, and dental coverage
- Flexible PTO
- Latest Apple equipment
- 401k plan with match!

## About Eventual

## Company Overview
- **One-liner**: Eventual builds an open-source, high-performance data engine (Daft) purpose-built for AI and multimodal workloads, making querying images, video, audio, and text as intuitive as working with tables.
- **Entity Type**: Private (Startup, Y Combinator W22 batch)
- **Headquarters**: San Francisco, California, USA (Mission District)
- **Founded**: 2022
- **Founders**: Sammy Sidhu (CEO) and Jay Chia

## Core Business
- **Primary Industry**: AI Infrastructure / Data Engineering / Multimodal Data Processing
- **Target Customers**: B2B, Enterprise AI teams, foundation model developers, autonomous vehicle companies, and any organization processing large-scale multimodal data (images, video, audio, text).
- **Mission / Purpose**: To make querying any kind of data—images, video, audio, text—as intuitive as working with tables, and powerful enough to scale to production AI workloads.

## Products & Services
- **Daft (Open-Source Engine)**: The company’s core product. A high-performance, distributed data engine designed for AI and multimodal data. It handles petabytes of data daily, coordinates with external APIs, manages GPU clusters, and handles failures that traditional engines (like Spark/Databricks) cannot. Available as an open-source Python library on GitHub (5,570+ stars).
- **Daft CLI**: A command-line tool for spinning up and managing Ray clusters for the Daft Query Engine.
- **Daft Examples & Benchmarking**: Public repositories providing usage examples and distributed query benchmarking tools.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed.
- **Key Metric**: Total Funding – Backed by Y Combinator, Caffeinated Capital, Array.vc, and angel investors including the co-founders of Databricks and Perplexity. Specific funding amounts are not publicly disclosed.
- **Notable Investors / Partners**: Y Combinator (Winter 2022 batch), Caffeinated Capital, Array.vc. Key customers/partners include **Amazon**, **Mobileye**, **Together AI**, and **CloudKitchens**.
- **Growth Signals**: Quadrupled team size within the past year (from ~4 to ~18 employees). Daft already processes petabytes of data daily at major companies. Actively hiring to double the team again.

## Competitive Advantages
- **Purpose-Built for AI**: Unlike Databricks, Snowflake, or Spark, which were designed for tabular/analytics workloads, Daft is built from the ground up for multimodal AI data (images, video, audio, text).
- **Open-Source + Performance**: Daft is open-source (Apache 2.0), giving teams full control, while delivering performance competitive with proprietary engines. It handles GPU cluster orchestration and API coordination natively.
- **Founding Team & Advisors**: Founders have deep backgrounds in HPC, deep learning, self-driving cars (DeepScale/Tesla, Lyft L5), and ML infrastructure (Freenome). The team includes engineers from Databricks, AWS, Nvidia, Pinecone, and GitHub Copilot.
- **Traction with Top-Tier Customers**: Already deployed at Amazon, Mobileye, Together AI, and CloudKitchens, validating product-market fit with demanding enterprise workloads.

## Strategic Focus
- **Scale the Team**: Aggressively hiring across engineering (Product, Systems, HPC, Research) to double the current headcount of 18.
- **Expand Daft’s Capabilities**: Continue building out Daft’s multimodal query engine to handle even more complex AI workloads and modalities.
- **Deepen Enterprise Adoption**: Grow usage within existing customers (Amazon, etc.) and acquire new large-scale AI teams.
- **Community Growth**: Grow the open-source community around Daft (currently 5,570+ stars on GitHub).

## Why Work Here
- **High Impact**: Work directly on infrastructure that powers breakthrough AI applications (foundation models, autonomous vehicles). “Your work directly determines whether AI teams can build breakthrough applications or get stuck rebuilding infrastructure.”
- **World-Class Team**: Join a small, elite team of 18 engineers from Databricks, AWS, Nvidia, Pinecone, GitHub Copilot, and Tesla. “Quadrupling our size within a year” signals rapid scaling and opportunity.
- **In-Office Culture**: 4 days/week in the San Francisco Mission District office. Tight-knit, collaborative environment with catered lunch/dinner, poker nights, and team-building events.
- **Compensation & Benefits**: Competitive salary ($150K–$250K for engineering roles) + startup equity. Benefits include health/vision/dental, flexible PTO, 401k with match, commuter benefits, and latest Apple equipment.
- **Values-Driven**: “Take pride in our work,” “Create clarity,” “Be curious,” “Own the outcome.” Emphasis on writing high-quality code, tackling hard distributed systems problems, and direct ownership.
- **Growth Trajectory**: Early-stage (18 people) with strong traction and backing. Opportunity to shape the product and culture as the company scales.

## Sources
1. [Y Combinator – Eventual Company Profile](https://www.ycombinator.com/companies/eventual)
2. [Eventual Official Careers Page](https://www.eventual.ai/careers)
3. [Eventual GitHub Organization](https://github.com/eventual-inc)
4. [Y Combinator – Eventual Jobs Page](https://www.ycombinator.com/companies/eventual/jobs)
5. [Eventual Job Board (Ashby)](https://jobs.ashbyhq.com/eventualcomputing)

## Other roles at Eventual

- [Software Engineer, Multimodal Backend Systems](https://feeny.ai/job/software-engineer-multimodal-backend-systems-eventual-san-francisco-szayste712fy) — San Francisco, CA
- [Research Engineer, Multimodal Data](https://feeny.ai/job/research-engineer-multimodal-data-eventual-san-francisco-q7myvdm4xpe4) — San Francisco, CA
- [Software Engineer, Data Systems](https://feeny.ai/job/software-engineer-data-systems-gamma-san-francisco-9rdvj51h48p4) — San Francisco, CA