Senior/Staff Machine Learning Engineer, River Financial

• 4+ years of experience building and applying machine learning models in real-world settings, or comparable research experience
• Strong intuition for ML concepts and how they apply in practice (i.e., tradeoffs)
• Experience working on problems involving noisy, imbalanced, or real-world data
• You take ownership of systems and are comfortable solving ambiguous problems
• You have strong judgment around correctness, reliability, and operational risk
• You can translate between technical systems and business/product needs
• You're excited about what we are building at River

Desirable

• We are looking for a Senior/Staff Machine Learning Engineer to build and own machine learning-driven systems that power automation and decision-making across onboarding, risk, compliance, and operations. You will apply ML and LLMs in production to solve real-world problems. Our core stack includes XGBoost, PyTorch, Python, MLflow, Postgres, and BigQuery
• Design, build, and iterate on machine learning models and LLM-based systems that power critical decisions across fraud, compliance, growth, and operations
• Work with messy, real-world data to identify signals, build features, and continuously improve model performance
• Make practical tradeoffs between model performance, interpretability, and operational cost
• Partner closely with product and operations to identify and solve problems that directly impact experience of hundreds of thousands of clients
• Contribute to backend systems and data pipelines that support model training and inference (without being primarily an infrastructure role)
• Write high-quality, tested code and participate in code reviews
• Take long-term ownership of critical systems as River scales