Senior Staff Machine Learning Engineer

Role overview

Join Affirm as a Senior Staff Machine Learning Engineer to help shape long-term machine learning strategy for the financial services industry. You’ll design, implement, and scale advanced modeling approaches that drive critical company decisions, while mentoring senior engineers and influencing technical architecture.

Key missions

• Contribute to the design, implementation, and scalability of advanced modeling approaches that impact core business decisions.
• Mentor senior engineers and bring clarity to complex technical problems.
• Define and lead a multi-year, cross-team ML technical strategy, aligned with company priorities.

Responsibilities

• Lead end-to-end ML system design: data architecture, feature pipelines, training, evaluation, and production deployment.
• Scale distributed ML infrastructure including ingestion (streaming/batch), feature stores, feature engineering, training pipelines, model serving/inference, monitoring, and automated retraining.
• Provide technical leadership by setting long-term strategy, guiding research direction, and aligning work across teams.

Requirements

• 10+ years of experience researching, designing, deploying, and operating large-scale real-time ML systems.
  • Relevant PhD may count for up to 2 YOE.
• Deep hands-on experience with distributed ML infrastructure and the full ML lifecycle (ingestion → features → training → serving → monitoring → retraining).
• Experience with distributed data/ML frameworks such as Spark or Ray.
• Strong communication skills and ability to collaborate with engineers, researchers, and executives.
• Proficient in Python and ML frameworks including PyTorch and XGBoost.
• Experience with ML tooling for training orchestration, experimentation, and monitoring such as Kubeflow, MLflow, or similar platforms.
• Strong expertise in representation learning / embedding-based modeling.
• Deep knowledge of neural network sequence modeling, including Transformers, recurrent/attention-based models, and multi-task learning.
• Ability to design and optimize models for sequential/temporal event data at scale.

Nice-to-haves

• A Bachelor’s degree in a related field (or equivalent practical experience).