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 / product, engineering, and data teams / aligned metrics and business impact Define data strategy/feature engineering and data sourcing / improved model performance and coverage Ensure model governance/data quality and integrity / trusted and compliant ML systems Mentor team members / technical guidance and best practices / elevated team capability and output quality Optimize model performance/tuning and retraining strategies / sustained accuracy and efficiency Evaluate new approaches/algorithms and technologies / continuous innovation and competitive advantage Communicate insights/model performance and impact / informed stakeholder decision-making.
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