Software Engineer (Machine Learning)

• Develop and improve user signals, features, and representations used by large-scale machine learning models for advertising and recommendation
• Explore machine learning approaches to learn effectively from large-scale, sparse, noisy, and heterogeneous user signals
• Improve the quality, coverage, and utilization of user signals, and measure their impact on downstream machine learning models and advertising performance
• Develop user representations and modeling approaches that effectively incorporate user signals into ranking, retrieval, prediction, and optimization systems
• Advance large-scale recommendation systems across candidate retrieval, ranking, prediction, and optimization
• Explore new model architectures and learning approaches to improve recommendation quality and advertising performance
• Develop scalable approaches for representation learning, feature interaction, and multi-task learning across large-scale user signals
• Identify and solve challenging ML problems spanning user signal quality, feature quality, model quality, training stability, data integrity, and serving performance
• Scale machine learning models and training systems to support increasing data volume, model complexity, and computational requirements
• Improve training and inference efficiency by identifying bottlenecks across model computation, data loading, memory utilization, distributed execution, and hardware utilization
• Build scalable tools and frameworks for user signal and feature evaluation, model training, experimentation, deployment, monitoring, and debugging
• Design and analyze offline and online experiments to understand the incremental value of user signals and model improvements and their impact on product and business outcomes
• Work closely with engineering, data, and product teams to bring new user signals and machine learning approaches from experimentation into production

Benefits

• 100% company paid health benefits for employees & dependent
• Phone bill covered for business usage
• Access to mental health and wellness provider
• Flexible time off
• Employee stock purchase plan
• Retirement benefits with company matching
• Parental leave program
• Life and disability coverage
• Family forming & women’s reproductive health benefits
• Team-centric company events and more!

• 4+ years of experience developing and deploying machine learning systems in production environments
• Bachelor’s degree in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience
• Strong programming and software engineering skills, with experience building reliable production systems
• Experience developing and training machine learning models using large-scale datasets
• Strong understanding of machine learning fundamentals, including model architectures, optimization, representation learning, feature engineering, and model evaluation
• Experience diagnosing and solving problems involving data and feature quality, model quality, training, or serving performance
• Experience with machine learning or deep learning in areas such as recommendation, ranking, retrieval, prediction, advertising, representation learning, or related applications
• Experience with modern deep learning frameworks such as PyTorch or TensorFlow
• Experience with large-scale recommendation or advertising systems, including candidate generation, retrieval, ranking, or prediction
• Experience developing user signals, features, or learned user representations for large-scale machine learning systems
• Experience with representation learning, embeddings, feature interaction, or multi-task learning using large-scale user signals
• Experience measuring the incremental value of user signals and understanding their downstream impact on ranking or recommendation performance
• Experience developing and scaling deep learning architectures for recommendation, ranking, or advertising applications
• Experience with distributed model training and large-scale ML infrastructure
• Experience optimizing training or inference workloads on GPUs or other accelerators
• Experience optimizing ML systems for latency, throughput, memory utilization, or computational efficiency
• Experience designing and analyzing online experiments and offline model evaluations