Machine Learning Engineer
Air Space Intelligence1 year ago
Gdańsk, PolandMid Level
Responsibilities
- Design and deploy production-grade systems that integrate machine learning models into scalable software pipelines.
- Develop and ship ML-powered features for real-world optimization and prediction problems.
- Build and maintain scalable, reliable production ML systems and robust data pipelines.
- Apply software engineering practices that prioritize robustness, maintainability, and performance at scale.
- Develop production LLM capabilities involving prompt engineering, fine-tuning, and RAG systems.
Requirements
- Proficiency in Python and production ML tooling and frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Experience using LLMs in production, including prompt engineering, fine-tuning, RAG systems, and LangChain.
- Strong understanding of data structures, algorithms, and software engineering best practices.
- Familiarity with classical machine learning, transformer-focused deep learning, and MLOps concepts.
- Experience building and maintaining scalable, reliable production ML systems and robust data pipelines.
- Expertise with Apache Beam, MLflow, and similar production-grade tools.
- Knowledge of data versioning, experiment tracking, model governance, and automated testing pipelines.
- Ability to solve complex problems with simplicity and clarity and collaborate across cross-functional teams.
- Clear communication skills and intellectual curiosity.
Benefits
- Flexible working hours
- Medical, dental, and vision coverage for employees and dependents
- Competitive salary and equity
- Generous relocation package to the Tricity area
- Flexible time off
- Office equipment and tools, including ergonomic desk setups and modern productivity software
- Team breakfast and lunch prepared daily
Tech Stack
Categories
About Air Space Intelligence
Air Space Intelligence builds decision-support software that lets operators simulate scenarios, predict risk, and optimize logistics and operations across aviation, defense, energy, and other critical infrastructure. The company sells enterprise platforms to government and commercial customers, including programs requiring U.S. security clearances. Founded in 2018 and headquartered in Boston, it is privately held and backed by investors such as Andreessen Horowitz, Spark Capital, and Renegade Partners.