Machine Learning Engineer

As a Senior Machine Learning Engineer, you’ll play a key role in turning innovative ML research into scalable, real-world solutions that power global decision-making. At NiQ, we’re looking for someone to join our Tech & Durable Global Data Science team, working at the intersection of data science and engineering to transform cutting-edge research into robust, production-ready systems.
If you're excited by cloud technologies, MLOps, and solving complex problems with smart, data-driven approaches, and you thrive in a collaborative, learning-focused environment, this role is for you.
What You’ll Do
Build & Scale ML Systems: Design, develop, test, deploy, and maintain machine learning solutions using software engineering best practices.
Productionize Prototypes: Transform data science models into scalable, production-ready systems for real-world applications.
Own the ML Lifecycle: Implement and manage end-to-end ML workflows using MLOps practices in both cloud and on-prem environments.
Collaborate Globally: Partner with data scientists, software engineers, and product experts in cross-functional, international teams.
Drive Engineering Excellence: Develop and refine tools, methods, and best practices to elevate ML engineering standards.
Mentor, Share & Grow: Support team development through mentoring, contribute to internal Communities of Practice, and engage in training and cross-functional learning opportunities.
Shape the Future of ML: Influence the direction of ML engineering at NIQ by contributing to strategic initiatives and technical roadmaps.
Tech Stack You’ll Work With
Languages & Frameworks: Python, SQL, Argo, Kubeflow, MLflow
CI/CD Tools: GitLab CI
Monitoring: Prometheus, Grafana
Containers & Orchestration: Docker, Kubernetes
Databases: PostgreSQL, BigQuery, RDBMS
Must-Haves
Degree in computer science, engineering, statistics, or a related field (BSc, MSc, or PhD).
4+ years of experience in machine learning software development.
Strong Python skills and experience with ML libraries and frameworks.
Solid experience working in large-scale database environments.
Knowledge of containerization and orchestration (Docker, Kubernetes).
Solid experience with production-level code quality and collaboration with software/testing engineers.
Solid understanding of statistical methods and machine learning algorithms
Excellent stakeholder management and communication skills to align technical solutions with business needs.
Ability to work independently and asynchronously as part of a distributed team
Professional working proficiency in English
Nice-to-Haves
Experience with cloud environments (AWS, GCP)
Familiarity with MLflow or similar ML lifecycle tools.
Experience with agile development practices.
Background in forecasting, pricing, revenue assurance, or media analytics is a plus.

Employment type
Full-time

Work arrangement
No