Full-stack Machine Learning Engineer

Role Overview

We are seeking a versatile Full-Stack Machine Learning Engineer to join our core technical team. This role is ideal for someone eager to build sophisticated production systems and grow with a small, focused team. You will work across the entire stack—from data ingestion and feature engineering to model training, validation, and deployment.

Responsibilities

• Work across the entire machine learning stack, from data ingestion to deployment.
• Develop, validate, and deploy machine learning models, with a focus on time series forecasting and quantitative applications.
• Design and implement robust data pipelines that handle real-world complexities such as missing data and schema changes.
• Independently make architectural decisions and own systems end-to-end.
• Engage in workflow orchestration and data quality monitoring.
• Maintain high engineering standards, ensuring code quality, observability, and reproducibility.

Required Skills

• Python: Professional-level proficiency with a deep understanding of libraries like NumPy and Pandas, including memory management and optimization.
• Machine Learning: Hands-on experience with the full lifecycle of ML systems, including model selection, hyperparameter optimization, and maintenance.
• Data Pipelines: Experience designing robust pipelines with workflow orchestration tools such as Airflow, Prefect, or Dagster.

Nice to Have

• C++: Experience in performance-critical components and interfacing with Python is a significant advantage.
• Multi-Agent Orchestration: Experience with LLM-based automation and multi-agent frameworks like LangChain or LangGraph.
• Domain Experience: Background in quantitative finance, algorithmic trading, fintech, or cryptocurrency markets.
• Education: University degree in a quantitative/technical field such as Computer Science, Mathematics, Statistics, Physics, or Engineering.

Clarify Ambiguities

• Location: Open to any, with a preference for candidates in the European time zone.
• Required Skills: Strong emphasis on Python and machine learning expertise, particularly in time series forecasting.
• Nice to Have: Additional skills in C++, multi-agent orchestration frameworks, and domain experience in financial data and crypto are beneficial but not mandatory.