machine learning engineer for Ads

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• Set the technical direction of multiple team pillars
• Lead cross-functional ML projects end to end, from business gap analysis to engineering execution
• Provide technical leadership and mentorship to MLEs, drive design reviews, establish best practices in analysis, modeling, and engineering, and maintain a high quality bar
• Develop technical strategy for content-based embeddings and Ads relevance with team and organizational leadership
• Develop and iterate on embedding models for advertising use cases, from aggregation pipelines to two-tower architectures and sequence models
• Work with local and third-party LLMs/VLMs to extract representations, develop evaluation methodologies, prompt-tune and fine-tune large models, and build state-of-the-art embeddings
• Build data processing and inference pipelines for developed models
• Evaluate features qualitatively and quantitatively, from internal benchmarks to downstream recommender system offline metrics and online experiments
• Ensure ML system reliability, scalability, and performance through automated tests, performance monitoring, and model management best practices
• Review modeling and code, and provide feedback to ensure quality and performance standards
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions
• Spend approximately 50% of the role on technical leadership and mentorship, and 50% on hands-on data analysis, engineering, modeling, and automation

• 7+ Years of hands-on experience designing, training, evaluating, testing, and deploying industry-level models across the full lifecycle
• Demonstrated Staff-level technical leadership, including mentoring engineers, driving standards and quality, and leading complex cross-functional projects from requirements and design through cross-team alignment and execution, without direct people-management authority
• Excellent verbal and written communication skills, including the ability to explain complex technical concepts to different audiences
• Strong track record working on content-rich NLP/CV problems at scale and using embeddings to solve them
• Data-driven approach to ML system development and readiness to investigate the data behind metrics
• Familiarity with the Ads domain and/or Search/Recommender systems
• Experience with PyTorch or TensorFlow
• Будет плюсом: experience with Python, Pytorch, Airflow, BigQuery, Ray, k8s, kafka, and GCP; technical leadership through mentoring junior engineers and leading complex projects; hands-on experience using, fine-tuning, or building LLMs

• Global benefits programs covering workspace, professional development, and caregiving support
• Family planning support, gender-affirming care, mental health and coaching benefits
• Group Personal Pension Scheme with employer match, private medical and dental scheme, and income replacement programs
• Bike to Work scheme, flexible vacation, paid volunteer time off, and generous paid parental leave
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