Senior Machine Learning Engineer

Job purpose:

Lead the design, development, and scaling of advanced machine learning systems that power Gathern’s marketplace intelligence across discovery, pricing, personalization, and trust domains. The role combines deep technical expertise with strategic thinking to translate complex data into production-grade predictive and decisioning systems. Focuses on architecting scalable ML infrastructure, ensuring model robustness and reliability, and driving measurable business impact through experimentation, optimization, and cross-functional leadership.

Key accountabilities:

• Architect ML systems, end-to-end pipelines, scalable and production-ready solutions.
• Lead model development, pricing and ranking algorithms, improved conversion and revenue yield.
• Design forecasting solutions, demand and supply models, optimized inventory and occupancy planning.
• Enhance risk models, fraud detection systems, reduce cancellations and policy violations.
• Establish MLOps standards, deployment and monitoring frameworks, reliable and maintainable model operations.
• Drive experimentation, A/B testing and validation frameworks, data-driven product improvements.
• Collaborate cross-functionally with product, engineering, and data teams to align metrics and business impact.
• Define data strategy, feature engineering and data sourcing to improve model performance and coverage.
• Ensure model governance, data quality and integrity for trusted and compliant ML systems.
• Mentor team members by providing technical guidance and best practices to elevate team capability and output quality.
• Optimize model performance through tuning and retraining strategies for sustained accuracy and efficiency.
• Evaluate new approaches, algorithms, and technologies to drive continuous innovation and competitive advantage.
• Communicate insights on model performance and impact to support informed stakeholder decision-making.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• 5–8+ years of experience in applied machine learning, data science, or ML engineering roles.
• Proven experience designing, deploying, and scaling ML systems in production environments.
• Strong experience with large-scale data processing and distributed systems (e.g., Spark, Beam).
• Advanced proficiency in Python and ML frameworks such as TensorFlow or PyTorch.
• Deep understanding of MLOps practices (CI/CD, model lifecycle management, feature stores, monitoring).
• Strong expertise in SQL and data engineering tools (Airflow, Kafka, DBT, etc.).
• Experience with experimentation frameworks and causal inference methods is a plus.
• Strong system design and architecture skills for ML-driven products.
• Excellent problem-solving skills with a strong analytical and business-oriented mindset.
• Ability to lead initiatives, influence stakeholders, and mentor junior team members.