Role overview
Published via Mainder
We are looking for a Senior Machine Learning Engineer to design and deliver advanced, production-grade ML systems that solve complex business problems at scale.
In this role, you will be part of a highly senior technical team, contributing to the design, development, and deployment of end-to-end machine learning solutions in enterprise environments. You will work closely with cross-functional stakeholders to translate ambiguous business challenges into scalable AI systems.
This is a hands-on role requiring strong expertise in advanced statistics, experimentation, deep learning, and ML systems architecture. You will contribute to AI evaluation standards, support governance practices, and help scale validated solutions across teams.
Design, build, and deploy end-to-end ML systems in production environments
Address complex ML challenges such as imbalanced data, concept drift, and large-scale experimentation
Apply advanced statistical techniques including Bayesian methods, mixed models, survival analysis, and causal inference
Design and analyze experimentation frameworks including A/B testing and multi-armed bandits (MABs)
Develop and optimize deep learning architectures for unstructured data (e.g., NLP, Computer Vision)
Contribute to scalable ML pipelines using cloud-native ML platforms
Collaborate with data and engineering teams to architect robust data integration strategies
Support AI evaluation, validation, and governance processes
Rapidly prototype solutions to validate hypotheses before scaling
Partner with business stakeholders to clarify problem definitions and ensure measurable impact
Bachelor’s degree in Computer Science, Engineering, or a related field
8+ years of experience delivering complex ML or AI initiatives in production environments
Strong experience designing end-to-end ML systems for real-world problems
Proven experience handling imbalanced datasets and concept drift
Deep understanding of causal inference and experimental design
Advanced statistical knowledge including Bayesian approaches, mixed models, and survival analysis
Experience implementing deep learning architectures for NLP and/or Computer Vision
Strong proficiency in Python (Pandas, NumPy) or R
Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML Services)
Experience with big data technologies such as Spark or Hadoop
Familiarity with MLOps tools (Kubeflow, MLflow, Docker)
Experience integrating complex enterprise data systems
Strong understanding of data governance and data quality frameworks
Experience working with modern data warehousing or lakehouse architectures
Strong ability to operate in ambiguous environments and structure complex problems
Experience working in cross-functional teams with engineering, product, and business stakeholders
Strong communication skills in technical and non-technical contexts
Opportunity to work on high-impact AI initiatives in enterprise environments
Collaboration within a highly senior technical team
Exposure to complex real-world ML challenges
Environment that values ownership, experimentation, and continuous improvement
Career growth aligned with technical depth and impact
We are a technology-driven organization focused on building scalable, high-impact AI and data solutions across complex enterprise environments. Our culture values technical excellence, intellectual curiosity, and collaboration.
Engineers are empowered to contribute meaningfully to product and technical decisions while solving real-world challenges through advanced data and AI systems.