Data Infrastructure Engineer

Reposted Yesterday
San Francisco, CA, USA
In-Office
140K-180K Annually
Mid level
Artificial Intelligence • Big Data • Computer Vision • Machine Learning
The Role
The Data Infrastructure Engineer will build backend architecture, manage data pipelines, and ensure high-performance compute clusters to support machine learning workloads.
Summary Generated by Built In
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
  • Competitive equity compensation at a seed stage startup

  • Options for housing support

  • Visa sponsorship

  • 3% 401k matching

  • Health insurance

Skills Required

  • 3+ years of production software engineering experience
  • Deep expertise in systems-level architecture and languages like Python, Rust, C++, or Go
  • Built and maintained high-performance ETL pipelines for processing terabytes of data
  • Architecting, provisioning, and maintaining bare-metal local compute clusters
  • Managing databases like TimescaleDB and ClickHouse
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The Company
HQ: San Francisco, CA
9 Employees
Year Founded: 2023

What We Do

Alljoined develops neural decoding technology to interpret brain signals non-invasively, aiming to bridge the gap between human thought and technology by applying deep learning research to EEG datasets to decode images, emotions, and thoughts.

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