Dateningenieur:in

Syn2core; München; On-site; EUR 90,000 - 120,000; Full time

Job summary

Syn2core is building an AI Copilot on smart glasses to assist assembly-line workers in real time, processing multi-stream data and providing audio feedback on the line.

You will own the end-to-end data flywheel—ingestion, labeling, and lineage—enabling ML engineers to build models with traceable training data, while upholding the privacy promise across factory devices.

Qualifications

• Strong Python proficiency and production-grade data pipelines.
• Experience with both relational and non-relational databases.
• Hands-on experience with object storage at scale and data pipelines.
• Experience with data serialization formats (protobuf, Avro).
• Knowledge of schema design and migrations (e.g., Alembic).

Responsibilities

• Own the data flywheel from logging on edge devices to labeled, queryable datasets.
• Ensure data anonymization and privacy at ingest; verify lineage.
• Build export/encoding pipelines for egocentric robotics datasets for research.

Skills

Python

Relational DBs

Non-relational DBs

Object Storage

Data Pipelines

Protobuf / Avro

Alembic

Tools

Label Studio

FiftyOne

C++

Rust

Job description

At Syn2core, we're building AI Copilot, an AI assistant that runs on smart glasses to support assembly-line workers in real time. Our system streams and processes data in real time, and delivers audio feedback directly to the worker. We're deploying it on the production lines of the automotive industry across the world— privacy-first by design: everything we persist is anonymized at ingestion.

Our long-term vision: the egocentric data we collect is training fuel for the next generation of robot learning – positioning us at the heart of the race towards autonomous robots for industrial deployment in automotive, aviation, aerospace and beyond.

The role

Everything we do runs on data recorded in the field: multi-stream video, audio, and events captured on factory devices, synced, labeled, and turned into training sets. You will own that flywheel end-to-end — from a byte logged on an edge device to a labeled, queryable, anonymized dataset an ML engineer can use without asking anyone. Its speed directly limits how fast we deliver to clients; its quality and lineage carry both our robot-learning research and our client acceptance evidence.

You'll work closely with the ML team (your customers) and the CTO, and it's a year-one mandate, not a day-one checklist — we'll build it with you.

What you'll do

• Own the flywheel — logging correctness across video, audio, events, and telemetry (timestamp discipline included); the datalake with its ingest, storage, and labeling tooling
• Make data self-serve — ML engineers assemble datasets without a human intermediary; every deployed model traces back to its exact training data
• Guard the privacy promise — verify anonymization at ingest, don't assume it
• Feed the research — build the export/encoding pipeline for our egocentric robotics dataset, the foundation of our robot-learning thesis

What you get

• Sole ownership of the foundation two missions stand on — client delivery today, robot-learning research tomorrow
• Schema and lineage decisions that compound for years, made by you, early
• Data most teams never see: real egocentric multimodal recordings from factory floors

Where you'll be in 12 months

A field recording becomes ready for labeling in hours. Dataset assembly needs nobody's help. Nothing gets lost, and every model that passed client acceptance has full, queryable lineage. That's what we'll build together — and it will be yours.

Who you are

• You treat data quality as the product, not a chore
• Privacy by default — you verify, you don't assume
• You take pride in invisible infrastructure others build on without thinking about it
• You anticipate what ML consumers need before they file the ticket
• You can debug a pipeline across five systems without owning any of them fully

Your experience

Must have:

• Strong Python
• Production experience with relational and non-relational databases
• Object storage and data pipelines at real scale
• Data serialization formats and their evolution (e.g., protobuf, Avro)
• Schema design and migrations (e.g. Alembic)

Ways to stand out:

• A system language (C++, Rust) alongside Python
• ML data labeling and curation tooling (e.g. Label Studio, FiftyOne)