Machine Learning Engineer

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Company Overview

A technology company specializing in large-scale artificial intelligence and machine learning solutions for digital advertising, commerce, and monetization. Its AI-powered platforms use massive amounts of first-party data to help businesses improve advertising performance, user acquisition, marketplace monetization, and measurable business outcomes. The company operates globally with teams across major technology and business hubs.

Responsibilities

• Architect and develop high-performance machine learning models that improve ad relevance, click-through rates, conversion performance, and marketplace outcomes.
• Build, productionize, and maintain scalable machine learning pipelines covering data ingestion, feature engineering, model training, and online inference.
• Analyze large-scale user behavior and auction datasets to develop new features and refine modeling strategies.
• Translate business objectives such as revenue, ROI, engagement, and user experience into measurable machine learning problems and success metrics.
• Design and evaluate experiments, including A/B testing, offline evaluations, and counterfactual analyses.
• Develop models that balance advertiser performance with marketplace health and user experience.
• Improve feature stores, data-quality monitoring, system observability, and debugging practices to maintain reliable machine learning systems.
• Contribute to machine learning platforms, tools, standards, and engineering practices that increase experimentation and deployment velocity.
• Optimize machine learning systems for low latency, high throughput, and efficient large-scale decision-making.
• Collaborate with infrastructure teams to improve system performance and reliability.
• Share technical knowledge, modeling practices, and production machine learning expertise with engineering teams.
• Take ownership of machine learning projects from initial problem definition through production deployment and ongoing optimization.

Qualifications

• 5–12+ years of experience delivering machine learning solutions in production environments, particularly in areas such as advertising, recommendation systems, search, marketplaces, or other high-scale applications.
• Strong foundation in machine learning and statistics applied to real-world business problems.
• Experience with areas such as ranking, calibration, exploration-exploitation, causal inference, or multi-objective optimization.
• Strong understanding of the full machine learning lifecycle, including data pipelines, feature engineering, model development, inference optimization, and production deployment.
• Proficiency in Python and SQL, with experience using machine learning frameworks such as TensorFlow or PyTorch.
• Experience with large-scale data processing technologies such as Spark, Beam, or Flink.
• Experience operating machine learning systems in production, including performance monitoring, drift detection, debugging, and continuous improvement.
• Ability to work with large-scale, low-latency machine learning workloads.
• Strong communication and technical collaboration skills.
• Ability to provide technical mentorship and contribute to engineering standards and best practices.
• Experience working with cloud and distributed infrastructure is highly valuable.

Working Approach

• Demonstrate ownership and take accountability for projects from problem definition through delivery and ongoing improvement.
• Work effectively in ambiguous environments by turning broad requirements into clear plans and practical technical decisions.
• Make thoughtful decisions when information is incomplete while balancing factors such as speed, quality, scalability, and long-term impact.
• Communicate clearly, document important decisions, surface risks early, and provide structure to complex technical discussions.
• Collaborate across engineering, product, data science, and infrastructure teams rather than working in isolation.
• Approach technical disagreements constructively and maintain productive professional relationships.
• Focus on solving underlying problems rather than applying short-term fixes.

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