8 days ago

Machine Learning Engineer

Marbl

On Site
Full Time
€75,000
Vienna, Austria

Job Overview

Job TitleMachine Learning Engineer
Job TypeFull Time
CategoryCommerce
Experience5 Years
DegreeMaster
Offered Salary€75,000
LocationVienna, Austria

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Job Description

Your Mission

You build and deploy models used directly in electricity trading. You will take ideas from research papers through implementation, large scale experimentation, and deployment into production systems that operate under real market constraints. You will work with large, high frequency time series data and ensure models are reproducible, testable, and monitorable.

Your Position

  • Develop and productionise ML models used for forecasting, optimisation, and decision support
  • Design and run ML experiments, training pipelines, and backtests
  • Collaborate with Data Engineers on data pipelines and feature generation
  • Ensure models are reproducible, testable, and monitorable in production
  • Write clean, maintainable, well tested production code

Your Profile

Core skills
  • Master's or PhD in a quantitative field (ML, statistics, physics, applied maths, engineering)
  • 3 or more years of experience as an ML Engineer, or Engineer/Researcher in a quantitative role
  • Proven ability to write clean production quality code
  • Strong experience with Git and Docker in ML workflows
  • Hands on experience running ML experiments, training, and backtests end to end
  • Comfortable with mathematical optimisation and training ML or DL models
  • Professional English (C1) is required
Nice to have
  • Academic or industrial research experience
  • Experience with deep learning, linear programming, stochastic optimisation, reinforcement learning, or time series analysis
  • Experience working with meteorological or weather data
  • Familiarity with trading or financial data, electricity markets, or power systems

Benefits

  • You move in flat hierarchies and have short decision making paths
  • End to end ownership from modelling through deployment
  • Close collaboration with a small senior team and fast iteration cycles
  • Flexible hybrid setup depending on location
  • Compensation is based on the IT collective agreement (38.5 hours/week), with willingness to overpay depending on experience and qualifications
  • Attractive employee participation is part of the package

About marbl

Electricity markets are entering a new era. As renewables, storage, and flexible demand scale, price dynamics get sharper, faster, and more volatile. Winning is no longer about reacting well, it is about making high quality decisions continuously. marbl is building the algorithmic flexibility trading layer for this market reality. We turn market data into forecasts and optimisation driven actions that can run live, so trading teams can scale short term decision making across portfolios without scaling headcount or operational risk. The outcome we are driving is simple: make flexible assets easy to trade well. Better decisions, better execution, stronger economics, and faster progress toward a low carbon power system.

Key skills/competency

  • Machine Learning
  • Forecasting
  • Optimization
  • Model Deployment
  • Production Code
  • Time Series Analysis
  • Experimentation
  • Git
  • Docker
  • Deep Learning

Tags:

Machine Learning Engineer
Machine learning
Forecasting
Optimization
Model deployment
Experimentation
Backtesting
Production code
Time series analysis
Data pipelines
Decision support
Python
Git
Docker
Deep Learning
Linear Programming
Stochastic Optimization
Reinforcement Learning

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How to Get Hired at Marbl

  • Research marbl's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
  • Tailor your resume: Highlight Machine Learning Engineer expertise, showcasing quantitative skills and production-grade coding.
  • Showcase production skills: Emphasize experience with Git, Docker, and deploying ML models in real-world systems.
  • Prepare for technical deep-dives: Focus on ML experimentation, time series analysis, and mathematical optimization challenges.
  • Demonstrate impact: Provide examples of taking research ideas to deployed, monitorable production systems.

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