Data & Machine Learning Engineer (All genders)

Meyandy LLC; München; Hybrid; EUR 70.000 - 100.000; Vollzeit

Zusammenfassung

STARK Munich is seeking a Data & Machine Learning Engineer to own data infrastructure and ML model development for the OAA team's AI use cases. You will build pipelines feeding models from operational and back-office sources, deploy models into production, and ensure reliability from yield prediction to anomaly detection.

Working with the OAA Lead and stakeholders, you will scope and validate ML use cases for feasibility and ROI, collaborate with the Automation Engineer to integrate outputs into

Qualifikationen

• 4–7 years of experience in data engineering or ML engineering.
• Experience deploying ML models to production.
• Proficiency in Python for data engineering and ML.
• Strong SQL for data extraction, validation, and pipelines.
• Experience with ML frameworks like scikit-learn or PyTorch.
• MLOps fundamentals including versioning, serving, monitoring, and retraining.
• MSc in Data Science, CS, Statistics, or equivalent.
• Nice to have: Airflow, dbt, or similar.
• Experience with industrial or financial time-series data.

Aufgaben

• Design and build data pipelines from operational and back-office sources.
• Develop ML models for production and back-office use cases.
• Deploy models into production with serving and monitoring.
• Scope ML use cases with stakeholders for feasibility and ROI.
• Collaborate to integrate model outputs into automated workflows.
• Maintain and improve deployed models as data evolves.
• Document pipelines, model architectures, and deployment configs.

Kenntnisse

Python

SQL

ML frameworks (scikit-learn, PyTorch)

MLOps

Data pipelines

Cloud platforms (AWS, GCP, Azure)

Time-series data

Ausbildung

MSc in Data Science, Computer Science, Statistics, or equivalent

Tools

Airflow

dbt

AWS

GCP

Azure

Jobbeschreibung

About Us STARK is a new kind of defence technology company revolutionising the way autonomous systems are deployed across multiple domains. We design, develop, and manufacture high-performance unmanned systems that are software-defined, mass-scalable, and cost-effective — providing operators with a decisive edge in contested environments. We are focused on delivering deployable, high-performance systems — not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe, today.

About the team

The Operations Excellence team sits within the COO organization and serves as a strategic partner to managers, team leads, and colleagues across Stark. By delivering data-driven insights, leading critical projects, and driving continuous process improvement, we help the organization operate more efficiently, scale effectively, and achieve its goals faster.As an individual contributor, you will take end-to-end ownership of complex initiatives with significant business impact. Working closely with cross‑functional stakeholders, you will have the opportunity to influence key decisions, shape core operating processes, and contribute directly to the success of one of Europe’s fastest‑growing unicorns.

Your mission

As Data & Machine Learning Engineer, you own the data infrastructure and ML model development for the OAA team's AI use cases. You build the pipelines that feed models with clean, reliable data from both operational systems and back‑office sources, deploy models into production, and ensure they perform reliably — from yield prediction on the line to anomaly detection in financial data.

Responsibilities

• Design and build data pipelines from operational (MES, ERP) and back-office sources feeding ML models


• Develop ML models for production and back-office use cases — from experimentation through to production deployment


• Deploy models into production: serving infrastructure, monitoring, drift detection, and retraining workflows


• Work with the OAA Lead and stakeholders to scope and validate ML use cases — feasibility, data availability, ROI


• Collaborate with the Automation Engineer to integrate model outputs into automated workflows


• Maintain and improve deployed models as data distributions and operational conditions evolve


• Document data pipelines, model architectures, feature definitions, and deployment configurations

Qualifications

• 4–7 years in data engineering or ML engineering


• Demonstrated experience deploying ML models to production: not just research or notebook-level work


• Python: core language for data engineering and ML development


• SQL: data extraction, validation, and pipeline development


• ML frameworks: scikit-learn, PyTorch, or equivalent


• MLOps fundamentals: model versioning, serving, monitoring, retraining


• MSc in Data Science, Computer Science, Statistics, or equivalent


• Nice to have Data pipeline tooling: Airflow, dbt, or equivalent


• Cloud data platforms: AWS, GCP, or Azure


• Experience with industrial, time-series, or back-office financial data

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