Data Engineer - Foundational

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Series B From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Data Engineer - Foundational Paris, Île-de-France, France 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

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