Sr. Engineering Manager , Machine Learning

Adobe is in search of a Senior Engineering Manager, Machine Learning, to lead a high-impact team dedicated to building and deploying brand-new ML solutions. You will be responsible for guiding the team through the full lifecycle of machine learning projects, from research and development to scalable deployment. Collaborate with various teams, like product and engineering managers, to innovate and advance custom products. 

This role requires deep expertise in ML and AI, strong leadership skills, and a track record of successfully delivering machine learning systems at scale. You will nurture a culture of experimentation, mentor a hard-working team, and contribute to the ML strategy on the Genuine Engineering team. 

Prevent account sharing, fake accounts, trial abuse, and payment fraud with Adobe's Engineering Team solutions. Using machine learning, behavioral analytics, and anomaly detection, we proactively combat misuse while maintaining a seamless user experience. 

Key Responsibilities 

• Lead, mentor, and grow a team of ML engineers to develop and deploy pioneering ML models. 
• Own the end-to-end execution of ML projects, from research and prototyping to production deployment. 
• Collaborate with product teams to identify AI/ML opportunities and define technical roadmaps. 
• Ensure ML models are scalable, efficient, accurate and aligned with business goals. 
• Adopt standard methodologies in ML model training, evaluation, and monitoring to ensure high performance and reliability. 
• Work with infrastructure teams to optimize deployment and inference for ML models. 
• Stay ahead of advancements in AI/ML and bring innovative solutions to Adobe's products. 
• Champion responsible AI practices, ensuring fairness, interpretability, and robustness in ML models. 
• Partner with leadership to align ML strategy with business objectives and give to long-term AI initiatives. 

Minimum Qualifications 

• proven track record in software engineering, with at least 5 years in machine learning and 3+ years leading teams. 
• Proficient in anomaly detection and fraud prevention, developing systems to identify account sharing, payment fraud, trial abuse, and synthetic identity fraud through behavioral analytics and real-time scoring models. 
• Bringing to bear experience in unsupervised and semi-supervised learning for anomaly detection, employing techniques like auto encoders, isolation forests, and graph-based fraud detection. 
• Experience applying NLP in fraud prevention, including fake account detection, identity validation, and abuse pattern recognition using transformer-based models. 
• Hands-on experience with ML frameworks such as Scikit-Learn, TensorFlow, and PyTorch. 
• Proficiency in Python, with experience in cloud platforms like AWS, Azure, or GCP. 
• Experience developing large-scale data pipelines in Spark. 
• Strong background in MLOps, model optimization, and deploying ML models at scale. 
• Proven track record to mentor engineers and build high-performing teams. 
• Experience working with complementary teams to develop solutions for products. 
• Strong problem-solving skills and ability to navigate sophisticated technical challenges. 

Preferred Qualifications 

• Knowledge of graph-based ML, personalization, and ranking algorithms. 
• Familiarity with distributed computing frameworks (Spark, Ray, or Dask). 
• Experience in bringing research into production in the domain of fraud detection.