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.

• Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, or a related technical field, or equivalent practical experience.
• 4+ years of experience developing and deploying machine learning systems in production environments.
• Experience with machine learning or deep learning in areas such as recommendation, ranking, retrieval, prediction, advertising, representation learning, or related applications.
• 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.
• Strong programming and software engineering skills, with experience building reliable production systems.
• Experience with modern deep learning frameworks such as PyTorch or TensorFlow.
• Experience diagnosing and solving problems involving data and feature quality, model quality, training, or serving performance.

• Experience developing user signals, features, or learned user representations for large-scale machine learning systems.
• Experience with large-scale recommendation or advertising systems, including candidate generation, retrieval, ranking, or prediction.
• 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.

CA Base Pay Range 221000 - 331000 USD