---
title: 'Developer Relations Engineer at Spiral'
canonical: 'https://feeny.ai/job/developer-relations-engineer-spiral-new-york-5dbcwx6syte4'
type: 'job'
last_seen: '2026-09-11'
---

# Developer Relations Engineer at Spiral

- **Company:** Spiral
- **Location:** New York, NY
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-10
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.ashbyhq.com/spiral/25987e82-23f8-4308-a76f-72c0c0f28d00

## Job description

## About Spiral

Spiral builds a fast analytical database for multimodal, multi-rate data streams, on top of the open source Vortex file format. Our users are AI/ML researchers and AI infra engineers developing models in complex domains, such as weather & climate, financial, time-series, genomics, point-clouds, videos, and images. They spend their days waiting on data loaders, writing video or sensor data pipelines, and watching expensive GPUs sit idle on I/O. We make that pain go away.

We're small, technical, and early. The way we win researchers & developers is by being useful to serious practitioners.

## The Role

This is our first DevRel hire, and it is a content and developer-experience role. You'll own the material that teaches researchers and training engineers how to get real work done with Spiral: cookbooks, worked examples, benchmarks, and reference pipelines grounded in datasets people actually train on. You might also conduct research on new applications of the core Spiral product.

You'll collaborate with the rest of the team to create the tightest possible feedback loop between developers/researchers and our product and docs. The job is to earn credibility with a technical audience by shipping things that are genuinely good.

## What you'll do

- Write technical cookbooks and end-to-end examples — video ingestion, GPU data loading for training, multimodal feature engineering — that a researcher can run and immediately understand.
- Build and maintain reference pipelines against real datasets (such as those on Hugging Face), and keep them working as the product moves.
- Produce credible benchmarks and the honest writeups that go with them.
- Be the developer's advocate internally: turn friction you and users hit into concrete product, client SDKs (e.g. pyspiral), and docs improvements.
- Answer real questions in the places our users already are (GitHub, community channels, conferences), and turn recurring ones into permanent docs.
- Represent Spiral at conferences.
- Give the occasional talk or workshop.

## Who you are

- You've done ML or training-infrastructure work yourself, and you've personally felt the data-loading / video-decode / GPU-utilization pain we remove.
- You write well about technical things. You can point us to things you've written.
- You're fluent in Python and comfortable in the PyTorch / Hugging Face / data-pipeline ecosystem.
- You have a working mental model of GPUs, training loops, and where the bottlenecks actually are.
- You are well connected in developer and/or AI communities.

## Nice to have

- Open-source contributions in relevant territory (PyTorch data / DataLoader, HF datasets, Ray Data, video/decode tooling, or similar).
- Experience with columnar / analytical data formats.
- An existing audience among ML practitioners — welcome, but genuinely secondary to the credibility above.
- Prior DevRel, AI research, or research-engineering experience.

What this role is not

- Not a social-media-growth or "personal brand" role
- Not primarily events and evangelism
- Not marketing-with-a-little-code. This is engineering-grade technical work.

## About Spiral

## Company Overview
- **One-liner**: Spiral builds the multimodal data platform for frontier AI, enabling machine-scale ingestion, storage, and query of video, images, audio, point clouds, sensor streams, tables, and text as a single, versioned, object-store-native system.
- **Entity Type**: Private (Series A; total funding reported as $42.5M per LinkedIn, conflicting with $22M per CB Insights)
- **Headquarters**: New York, New York, United States (with offices in London, UK)
- **Founded**: 2023
- **Founders**: Will Manning (CEO), Robert Kruszewski (President, Co-Founder), Nicholas Gates (CTO, Co-Founder)

## Core Business
- **Primary industry**: Data Infrastructure & Analytics, AI/ML Infrastructure
- **Target customers**: Frontier AI labs, research teams, and enterprise AI groups working at petabyte scale with multiple data modalities (computer vision, robotics, autonomous vehicles, etc.)
- **Mission or purpose statement**: Built for the machine consumer – a database that treats bytes and tensors as first-class citizens, not a human-facing SQL warehouse.

## Products & Services
- **[Spiral Platform](https://spiraldb.com/)**: The core product – a unified multimodal data platform that ingests raw object storage data and materializes model-ready tensors directly into accelerator memory. Supports declarative sampling, time-travel (`asof(T)`), and hybrid deployments across neocloud, tradcloud, and on-prem clusters.
- **[Vortex](https://github.com/spiraldb/vortex)**: An open columnar format created by the team and donated to the LF AI & Data Foundation. Designed for analytical queries and AI workloads, Vortex offers up to 38% smaller files and 10–25× faster decompression than Parquet with ZSTD.
- **Open-source compression libraries**: FastLanes, ALP, FSST, and others – Rust/Zig implementations of state-of-the-art integer and floating-point compression algorithms.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding – $42.5M (LinkedIn reports rounds including $4.5M seed in 2023, $5.5M seed early 2025, $16.1M Series A in 2024, and $16.5M Series A in 2025). A conflicting source (CB Insights) reports $22M total; both are cited.
- **Notable Investors/Partners**: General Catalyst, Amplify Partners, BoxGroup, Redpoint Ventures, Lux Capital (and others)
- **Growth Signals**: 72.7% headcount growth YoY (18 employees), 86.8% monthly website traffic growth, active hiring in London and New York. Vortex accepted as an incubation project at the Linux Foundation.

## Competitive Advantages
- **Multimodal-by-design**: Unlike traditional databases optimized for tabular data, Spiral natively stores and queries video, point clouds, audio, and tensors in a single tree with a unified query language.
- **Object-store native, hybrid architecture**: Eliminates the need to copy or denormalize data between storage and training pipelines.
- **Open-source core**: Vortex and related compression libraries are community-governed, lowering adoption risk and attracting top open-source contributors.
- **Reproducibility built-in**: Immutable raw bytes on typed coordinate frames with `asof(T)` time-travel for every modality.

## Strategic Focus
- **Frontier AI infrastructure**: Solving the bottleneck of moving petabytes of heterogeneous data to accelerator memory. Spiral targets the largest AI research orgs (Mistral appears as a community partner) and pretraining pipelines.
- **Open-source ecosystem growth**: Driving adoption of Vortex as the standard columnar format for AI, with integrations into Apache Iceberg and PyTorch.
- **Talent acquisition**: Aggressively hiring Rust, Zig, CUDA, and systems engineers to scale the platform.

## Why Work Here
- **Culture**: Deep-tech startup with a strong open-source ethos (Vortex, FastLanes, etc.). Company describes itself as “built for the machine consumer” – ideal for engineers passionate about high-performance computing, storage, and AI infrastructure.
- **Remote/Hybrid Policy**: Not explicitly stated, but offices in New York and London suggest a hybrid model; many technical hires are likely remote-friendly.
- **Notable Perks & Engineering Culture**: 63% of employees are in technical roles. Talent sourced from Palantir, Microsoft, Monzo, Carnegie Mellon, and LangChain. The engineering team writes Rust, Zig, and CUDA, and works on bleeding-edge compression and database internals. Small team (18 people) with major impact on the LF AI ecosystem.
- **Career Path**: Fast growth (72.7% headcount increase in a year) means early employees can shape product direction and architecture.

## Sources
1. [spiraldb.com](https://spiraldb.com/)
2. [linkedin.com/company/spiraldb](https://www.linkedin.com/company/spiraldb)
3. [github.com/spiraldb](https://github.com/spiraldb/)
4. [cbinsights.com/company/spiral-6](https://www.cbinsights.com/company/spiral-6)
5. [jobs.ashbyhq.com/spiral](https://jobs.ashbyhq.com/spiral)

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