Data Engineer - Foundational

About the role

ABOUT US

Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.

Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.

ABOUT THE ROLE

As a Data Engineer on the Foundational team, you will serve as the "plumber" for deep learning, building the massive, high-performance data infrastructure required to power our foundational models. Based in Paris, you will manage terabytes—and eventually petabytes—of raw, unstructured, and noisy video data (EO and IR). Your mission is to ensure our ML engineers spend their time designing architectures, not waiting for data loaders or wrangling corrupted files.

RESPONSIBILITIES

- Multi-Modal Ingestion Pipeline: Build ETL/ELT pipelines to extract, decode, and store raw Electro-Optical (EO) and Infrared (IR) video from field logs into highly optimised formats like WebDataset, TFRecords, or Parquet.

- Sensor Synchronisation & Alignment: Develop algorithms to programmatically synchronise EO and IR frames temporally and spatially to provide paired inputs for model training.

- High-Throughput Data Loading: Architect storage-to-GPU pipelines to ensure multi-node training clusters maintain >90% GPU utilisation without I/O bottlenecks.

- Distributed Processing: Write and optimise distributed data processing jobs using tools like Apache Spark, Ray, or Apache Beam to process thousands of hours of tactical video logs.

- Data Quality & Versioning: Implement automated quality checks to filter corrupted or blank frames and maintain 100% reproducible training runs through robust versioning and lineage tracking.

- Infrastructure Evaluation: Assess and implement advanced storage solutions (e.g., MinIO, S3 tiering) to manage growing datasets while optimising for cost and latency.

CANDIDATE REQUIREMENTS

- Educational Background: A BS or MS in Computer Science, Software Engineering, or Distributed Systems is highly preferred. Deep knowledge of operating systems, networking, and parallel computing is essential.

- Technical Experience: 5-6+ years of experience building and maintaining terabyte-scale pipelines for unstructured data (video, images, or point clouds).

- Performance Optimisation: Proven track record of maximising multi-node GPU utilisation and optimising data loaders for frameworks like PyTorch or JAX.

- Tooling Expertise: Strong command of distributed computing tools (Spark, Ray, Beam) and ML data versioning tools (DVC, Apache Iceberg, or Pachyderm).

- Adaptability & Ownership: A systems-thinker who thrives in a fast-paced startup environment and views messy data as an engineering problem to be solved via automation.