Staff Machine Learning Engineer, Financial Connections
Build and operate large-scale machine learning systems for financial data quality, transaction categorization, risk scoring, and enrichment. The role requires 10+ years shipping production ML systems, strong framework and pipeline expertise, and the ability to mentor engineers and collaborate across teams.
About the job
Responsibilities
- Design, build, train, evaluate, deploy, and own production ML models that improve transaction categorization, risk scoring, and financial data enrichment.
- Design and build large-scale ML systems operating on diverse financial data from thousands of institutions.
- Experiment and iterate on models using PyTorch, TensorFlow, and XGBoost to improve data quality and accuracy.
- Develop pipelines and automated processes for offline and online model training and evaluation.
- Integrate ML models into production systems and ensure scalability and reliability.
- Collaborate with product, data science, and engineering partners to identify ML opportunities.
- Engage with current ML/AI developments and turn innovative ideas into production solutions.
- Mentor engineers and contribute to the ML engineering culture.
Requirements
- 10+ years of industry experience building and shipping ML systems in production.
- Proficiency with PyTorch, TensorFlow, XGBoost, and Spark.
- Experience designing, training, evaluating, productionizing, and deploying ML models at scale.
- Experience orchestrating data pipelines and leveraging large-scale datasets.
- Strong collaboration skills, autonomy, responsibility, and an entrepreneurial mindset.
Nice-to-haves
- MS or PhD in ML/AI or a related field such as mathematics, physics, statistics, or computer science.
- Experience in fintech, open banking, or financial data.
- Experience with NLP, LLMs, or large-scale text classification.
- Experience with adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality.
- Track record of deploying ML systems that solve ambiguous business problems.
- Experience with deep learning architectures, including transformers.
Skills
PyTorch, TensorFlow, Xgboost, Spark, Machine Learning, NLP, LLMs, Text Classification, Deep Learning, Transformers, Data Pipelines, Model Deployment, Risk Modeling, Fraud Detection
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