Data Infrastructure Engineer

Alljoined

San Francisco (CA)

On-site

USD 140,000 - 180,000

Full time

14 days+

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Benefits offered by this job

Competitive equity compensation

Options for housing support

Visa sponsorship

3% 401k matching

Health insurance

Job summary

A technology startup in San Francisco is seeking a Data Infrastructure Engineer to build backend systems and manage the data lifecycle. You will be responsible for creating pipelines that process large datasets and ensuring the infrastructure supports world-class research efforts. Candidates should have over 3 years of production experience, expertise in languages like Python and C++, and a background in managing compute clusters. Competitive equity compensation and additional benefits are offered, including housing support and visa sponsorship.

Qualifications

3+ years of production software engineering experience with deep expertise in systems-level architecture.
Experience building and maintaining ETL pipelines for terabytes of unstructured data.
Comfortable managing local compute clusters and high-speed networking for ML workloads.

Responsibilities

Own the entire data lifecycle from building pipelines to managing compute clusters.
Work alongside researchers to ensure high-throughput and low-latency pipelines.
Define storage topologies and sync datasets between local servers and the cloud.

Skills

Production software engineering experience

Expertise in Python

Expertise in Rust

Expertise in C++

Expertise in Go

High-performance ETL pipelines

Architecting compute clusters

Managing databases

Handling concurrent data streams

Optimizing I/O bound operations

Tools

TimescaleDB

ClickHouse

AWS

GCP

Azure

FFmpeg

GStreamer

OpenCV

Job description

About Alljoined

Alljoined is creating a future where humans are fully understood and augmented by technology. Our work solves the communication bottleneck between humans and computers by decoding thoughts from the brain, entirely non-invasively. We apply deep learning research to large scale EEG datasets to decode multimedia input, eventually moving to internal thought. We are state‑of‑the‑art in capabilities and are fully vertically integrated. Our goal is to develop a general consumer interface to completely transform how we can live our lives.

We are actively growing our founding engineering team to build the underlying infrastructure that makes this ambitious future a reality.

About the Role

As a Data Infrastructure Engineer, you will build the backend and hardware architecture that allows us to do high‑quality and fast research. You'll be owning our entire data lifecycle, from building pipelines that process massive multimodal datasets (video, audio, text, time‑series) to provisioning and managing both cloud and bare‑metal compute clusters we use to train on it. You will be powering our foundational model training by bridging the gap between physical neuro hardware and our central repositories, working alongside world‑class researchers to ensure they have a high‑throughput, low‑latency pipeline straight to the GPUs.

You might be a good fit if you

Have 3+ years of production software engineering experience with deep expertise in systems‑level architecture and languages like Python, Rust, C++, or Go.
Have built and maintained high‑performance ETL pipelines capable of processing, buffering, and storing terabytes of daily unstructured data.
Are comfortable architecting, provisioning, and maintaining bare‑metal local compute clusters, storage servers, and high‑speed networking for intensive ML workloads.
Have a background in handling continuous, highly concurrent data streams from heterogeneous hardware peripherals without data loss.
Are capable of working across hybrid environments to define storage topologies, manage databases (TimescaleDB, ClickHouse), and sync massive datasets between on‑premise edge servers and the cloud (AWS/GCP/Azure).
Enjoy owning the entire technical lifecycle of infrastructure, from optimizing low‑level I/O bound operations to production deployment.

Strong candidates may have

A deep understanding of modern ML frameworks (PyTorch/TensorFlow) and know how to build datasets that maximize and saturate GPU utilization.
Experience managing networking for distributed GPU training (InfiniBand, RoCE) or optimizing zero‑copy networking and shared memory.
Built infrastructure involving programmatic video processing (FFmpeg, GStreamer, OpenCV)

Compensation Range

$140,000 - $180,000/year

While this represents our expected range based on market data, final compensation will be determined based on your specific skills and experience and may be outside this range.

Benefits