Data & Machine Learning Engineer

Quick Facts

• Role: Data & Machine Learning Engineer (Remote)

• Focus: Data pipelines, production ML models, and energy/real-time systems

• Start: As soon as the candidate completes the interview process

• Compensation: 22,000–28,000 PLN net monthly (B2B)

Description

As a Data & Machine Learning Engineer, you will take end-to-end ownership of data pipelines and bring advanced analytics and optimization models into production. The role bridges data engineering, applied machine learning, and energy systems, working closely with data scientists to move forecasting and optimization from notebooks to production. You will help build the data platform from scratch while making pragmatic, opinionated technical decisions.

Responsibilities

• Own end-to-end data pipelines for high-volume time-series feeds (telemetry, forecasts, energy market data)

• Ensure data is clean, validated, and reliably available to downstream systems

• Build data quality in by default (validation, anomaly detection, monitoring)

• Shape the data platform architecture as it grows (real-time responsiveness + heavy historical processing)

• Partner with data scientists to productionize forecasting and optimization systems

• Improve modeling and algorithms through code review, technical debate, and hands-on engineering

• Embed data and models directly into the core product

• Own and maintain integrations with external partners (e.g., EMS and trading partners)

Requirements

• 3–6 years in data/ML engineering or applied data science with delivery to production and ownership of outcomes

• Python fluency for production-grade, tested code

• Hands-on experience with ETL/ELT, data modeling, and time-series data

• Strong PostgreSQL (schema design and query tuning under real load)

• Treat data quality, validation, and monitoring as first-class engineering

• Comfortable running pipelines and models on AWS without relying on heavy abstraction layers

• Prior production experience running models and pipelines via APIs, batch jobs, or scheduled workflows

• Thrive in early-stage settings with pragmatic scoping and fast iteration

Benefits

• Remote work and early-stage engineering impact, building a data platform from scratch

• Opportunity to productionize advanced forecasting and optimization in an energy systems context