Staff Machine Learning Engineer, Financial Connections

What you'll do

• Design, train, evaluate, deploy, and own production ML models and large-scale ML systems.
• Develop pipelines for model training and evaluation, and integrate models into scalable, reliable production systems.
• Collaborate across teams, identify ML opportunities, and mentor engineers.

What they ask for

• 10+ years of industry experience building and shipping ML systems in production.
• Proficiency with PyTorch, TensorFlow, XGBoost, and Spark.
• Hands-on experience designing, training, and evaluating ML models and deploying them at scale.
• Experience orchestrating data pipelines and using large-scale datasets.

Nice to have

• MS or PhD in ML/AI or a related field; experience in fintech, open banking, financial data, NLP/LLMs, text classification, noisy-data domains, or transformers.
• Track record of deploying ML systems to solve ambiguous business problems.

Tools

PyTorch, TensorFlow, XGBoost, Spark

AI focus

Develop and productionize ML systems for transaction categorization, risk scoring, financial data enrichment, and data quality; preferred areas include NLP, LLMs, text classification, and transformers.