Senior Machine Learning Engineer

Smartstream; Wien; On-site; EUR 90,000 - 130,000; Full time

Job summary

SmartStream is seeking a Senior Machine Learning Engineer in Vienna to build and operate ML and AI solutions for financial data processing. You will turn prototypes into production-ready systems, working with large transaction datasets for matching, reconciliation, and exception handling.

You will collaborate with data scientists and software engineers, deploying end-to-end ML pipelines, models, and services with strong emphasis on observability, testing, and scaling.

Qualifications

• Degree in Computer Science, Engineering, Statistics, Mathematics, or related field.
• 4-6+ years in machine learning engineering or software engineering with an ML component.
• Experience delivering and operating ML models in production.
• Experience working in cross-functional teams delivering software products.

Responsibilities

• Develop, deploy, and maintain ML models and services, ensuring robustness.
• Translate research artefacts into production-grade ML systems with tests and observability.
• Own model serving, monitoring, drift detection, and retraining in production.
• Engineer features on real financial datasets and validate models for reliability.
• Collaborate with software engineers and data scientists on the platform.
• Document methods and decisions to keep models transparent and reproducible.

Skills

Python

ML Engineering

Data pipelines

CI/CD

Communication

Education

Bachelor's degree in Computer Science / Engineering / Math

Tools

PyTorch

FastAPI/Flask

Kubernetes

Docker

Job description

We are looking for a Senior Machine Learning Engineer to build, ship, and operate the machine learning and AI solutions at the core of SmartStream's financial data processing and reconciliation platforms. Working with our data scientists, you will turn prototypes into cohesive, production-ready systems, using large and complex financial transaction datasets to power capabilities such as transaction matching, reconciliation, and exception handling. You will work across the full spectrum of applied AI, from classical machine learning (supervised, unsupervised, and deep learning) to agentic AI solutions built on large language models, tool use, and multi-step reasoning.

Job Responsibilities

• Develop, deploy, and maintain machine learning models and services, and keep existing ones performant and robust
• Translate research artefacts and prototypes into production-grade ML systems: hardening code, adding tests and observability, and owning deployment, scaling, and lifecycle management
• Own model serving, monitoring, drift detection, and retraining in production
• Engineer and evaluate features on real financial datasets, and calibrate and validate models for reliable behaviour
• Collaborate with software engineers and data scientists on the surrounding data and matching platform
• Document methods and decisions to keep models transparent and reproducible

Requirements

• Strong software engineering in Python: clean, typed, well-tested code, version control, and CI/CD
• Strong proficiency with the scientific Python stack (NumPy, Pandas, scikit-learn, PyTorch) and a solid, practical grasp of machine learning, statistics, and model evaluation
• Experience taking ML models into production and operating them there (serving, monitoring, retraining), not just building them in notebooks
• Experience building and running production services and APIs (e.g. FastAPI or Flask), containerised and deployed on Kubernetes or similar
• Feature engineering on structured/tabular data, and sound model evaluation and validation
• Ability to work with large datasets and build reliable data pipelines
• Clear communication with technical and business stakeholders

Desirable Skills

• MLOps practices: model and data versioning, automated retraining, monitoring, and champion/challenger evaluation
• Distributed data processing (e.g. Dask, Spark, Apache Arrow/parquet) and handling columnar data at scale
• Deeper neural-network / PyTorch experience
• Model explainability (e.g. SHAP) and probability calibration
• Experience with LLM-based or agentic systems (tool use, orchestration, retrieval)
• Familiarity with workflow orchestration and event/stream processing is a plus
• Experience in regulated or data-intensive industries, ideally financial services
• Familiarity with cloud-based ML infrastructure

Qualifications

Degree in Computer Science, Engineering, Statistics, Mathematics, or a related field, or equivalent practical experience

Experience

• 4-6+ years in machine learning engineering, or software engineering with a strong ML component
• Experience delivering and operating ML models in production
• Experience working in cross-functional teams delivering software products
• Strong problem-solving skills and a pragmatic, ownership-driven approach to shipping reliable software

Equality Statement

Smartstream is an equal opportunities employer. We are committed to promoting equality of opportunity and following practices which are free from unfair and unlawful discrimination.