Data Engineer (Detect & Track Distillation)

Harmattan AI; Zürich; Hybrid; CHF 120.000 - 170.000; Vollzeit

Zusammenfassung

Harmattan AI is seeking a Data Engineer to own the data layer for our ML teams operating across Paris, Lausanne, or Zurich. You will transform raw field logs and public datasets into clean, versioned, training-ready data to accelerate model development.

You will build ingestion pipelines, align multimodal data, curate datasets, and define labeling requirements, collaborating with acquisition, annotation and product teams.

Qualifikationen

• Degree in a STEM field or equivalent practical experience.
• Experience building pipelines for unstructured data at scale.
• Strong Python and data engineering skills; comfortable with training frameworks like PyTorch.
• Ability to automate messy data and enable modelers.

Aufgaben

• Ingest, decode, and store raw unstructured field data from field logs.
• Temporally and spatially align multiple data streams for training.
• Curate data and public datasets through parsing, filtering, de-duplication, and revision.
• Define data to gather and labeling strategy; own dataset-construction workflow.
• Construct task-specific datasets with cross-functional teams.
• Version datasets and maintain lineage for reproducible runs.
• Store data in training-ready formats and manage storage tiering.
• Deliver clean datasets and tools for loading to keep training I/O efficient.

Kenntnisse

Python

Data pipelines

PyTorch

Data wrangling

Automation

Ausbildung

STEM degree or equivalent

Tools

SQL

Spark

Version control

Docker

Jobbeschreibung

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.

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

Our ML teams train models on datasets derived from large volumes of raw, unstructured data. Model quality depends directly on data quality, and today that data is handled largely by hand.

As a Data Engineer, operating out of Paris, Lausanne, or Zurich, you will own the data layer that feeds the team's models, from raw field logs or public datasets through curated, versioned, training-ready datasets. You will manage terabytes of raw, unstructured data and turn it into clean, documented, versioned datasets, so that the modelers spend their time designing and training models, not waiting on data loaders or wrangling corrupted files. You join at an early stage with real influence over how field and public data gets processed for deep-learning pipelines.

Responsibilities

• Ingestion Pipeline: Ingest, decode, and store raw, unstructured field data (video and other sensor streams) from field logs into efficient, controlled formats.
• Multimodal Alignment: Align multiple data streams temporally and spatially so paired data is usable for training.
• Curation: Transfer both ingested data and public datasets into high-value data, including parsing, filtering, de-duplication and revision.
• Data & Labeling Requirements: Define which data to gather and what and how to label it, and own the dataset-construction workflow and labeling tooling. Coordinate with the teams responsible for data gathering and labeling.
• Dataset Construction: Build task-specific datasets for the team’s training and evaluation needs, in collaboration with acquisition, annotation and product teams.
• Versioning & Lineage: Version datasets and maintain lineage so training runs stay reproducible.
• Storage & Formats: Store data in efficient, training-ready formats (such as columnar or sharded formats) and manage storage tiering to balance cost and latency as datasets grow.
• Efficient Delivery: Deliver clean, documented datasets and the corresponding tools for loading to keep training from being I/O-bound, shaping their structure with the modelers.

Candidate Requirements

• Educational Background: A degree in a STEM field, or equivalent practical experience. Practical data engineering experience matters more than the specific degree.
• Data Pipelines at Scale: Built and maintained pipelines for unstructured data at scale (video, images, or sensor data), covering ingestion, decode, storage, curation, and versioning.
• Engineering: Strong in Python and data engineering, and comfortable optimizing data loaders for common training frameworks (for example PyTorch).
• Bonus: Multimodal sensor data, labeling or dataset construction for ML, dataset versioning tooling, and distributed data processing tooling.
• Attributes: Systematic, quality-minded, pragmatic, and service-oriented so the modelers are enabled, with a knack for taming messy data via automation.
• Commitment: 100% dedication to Harmattan AI's mission of providing a defensive edge to allied nations through ethical, high-impact technology.

We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.