Senior Staff Machine Learning Engineer

We are looking for a Staff Machine Learning Engineer with strong data engineering expertise to join our team. In this role, you will shape the technical vision for our ML systems and act as a senior technical leader-designing production-grade pipelines, ensuring models are consistent, robust, and observable in production, and mentoring engineers. You will combine hands-on ML engineering with data platform expertise, building the foundations that enable us to scale. You will also lead a small team of 3-4 engineers, guiding their work, setting technical direction, and fostering growth and collaboration., * Lead the design and development of end-to-end ML pipelines: training, validation, deployment, and monitoring.

• Define and enforce data and model contracts, versioning, and reproducibility standards.
• Build and operate feature stores, model-serving infrastructure, and drift detection frameworks.
• Design parallel ETL and data pipelines that transform raw inputs into clean, model-ready datasets with clear SLOs.
• Ensure reliability, lineage, and quality of datasets used in production ML workflows.
• Lead and mentor a small team, driving execution, performance, and career development.
• Review architecture and code, set technical standards, and mentor engineers across the team.
• Partner with Product, Data Science, and Engineering to translate requirements into scalable ML/data solutions.
• Drive operational excellence: monitoring, alerting, incident response, and cost/performance optimization., * Flexible work options: remote within the EU with occasional visits to our Cologne HQ or Barcelona office or a hybrid setup in one of these locations.
• A dedicated learning budget to support your professional growth and development.
• Local language lessons (Germany and Spain).
• Health and fitness benefits (Germany and Spain).
• Personal and professional growth opportunities.
• Regular company events.

To ensure a smooth and efficient process and that no applications are overlooked, please apply directly to the role. While we'd love to respond to everyone who reaches out, applying ensures that your application is properly tracked and considered.

Do you have experience in Spark?, * 7+ years of experience building and deploying end-to-end ML solutions, ideally in a SaaS environment.

• Demonstrated ability to lead projects and guide engineers through strong technical leadership, complemented by experience in managing people and fostering team development.
• Proven track record of designing scalable, production-ready ML systems and pipelines.
• Strong expertise in MLOps, including experience with Docker, parallel ETL pipelines, distributed systems, and cloud-based data processing.
• Advanced programming skills in Python, with deep experience using ML libraries such as NumPy, Pandas, PyTorch, scikit-learn, and Pydantic.
• Hands-on experience with distributed compute frameworks (e.g. Spark, Flink) and workflow orchestration (Airflow, Dagster, Prefect).

Preferred Qualifications:

• TensorBoard experience.
• Experience with large-scale simulation, forecasting, or optimization systems.
• Domain expertise in retail/CPG data, pricing, or revenue management.

"What will customers buy?" is the crucial question underlying all commercial decisions in all companies. Buynomics has developed Virtual Shopper technology, which answers this question 100x faster and more precisely than every other solution. This will end the common practice of gut feeling. Our goal is to enable companies to make data-driven, transparent, and customer-centric commercial decisions. For that, we build the operating system for commercial decisions that provides companies with a single source of truth to answer all their market-facing questions including pricing, product offers, and promotions. Buynomics is the next-generation SaaS company in pricing and product optimization. It employs large-scale simulation technologies to digitize customers and predict their purchase behavior - in any environment. Our clients use Buynomics to make data-driven decisions about their current and new products, overall portfolio structure, pricing, promotions, trade terms, and more!