Senior/Staff Machine Learning Engineer

About the role

This role focuses on building and refining machine learning models and LLM-based systems that drive key decisions across various business functions, including fraud detection, compliance, growth, and operations. The work involves addressing practical challenges such as balancing model performance with interpretability and operational costs, and contributing to the backend systems and data pipelines that support model training and inference.

A core aspect of the position is working with real-world data, which often means dealing with noisy or imbalanced datasets to identify signals and continuously improve model performance. Success in this role requires a candidate to take ownership of critical systems as the company expands, writing high-quality, tested code, and participating in code reviews to maintain standards.

The engineer will partner closely with product and operations teams to identify and solve problems that directly affect the experience of hundreds of thousands of clients. This collaboration ensures that technical solutions align with business needs and product goals, requiring the ability to translate between technical systems and business requirements.

The salary for this role ranges from $150,000 to $250,000, dependent on skills and experience.

Skills that matter here

• XGBoost: This tool will be used for building and iterating on machine learning models to power critical business decisions.
• PyTorch: The role involves using PyTorch for developing and refining machine learning models and LLM-based systems.
• Python: Python is the primary language for writing high-quality, tested code and building machine learning solutions.
• MLflow: MLflow will be utilized in the machine learning workflow, likely for managing the lifecycle of models.
• Postgres: Postgres will be part of the backend systems and data pipelines that support model training and inference.
• BigQuery: BigQuery will be used within the data infrastructure to support machine learning initiatives.

Who this role suits

• You have at least four years of experience applying machine learning models in practical settings or comparable research.
• You possess a strong understanding of machine learning concepts and how to apply them effectively, including making necessary tradeoffs.
• You are comfortable working with complex, real-world data that may be noisy or imbalanced.
• You are proactive in taking ownership of systems and adept at navigating ambiguous problems.

From the employer

What you will be doing

• 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

What we look for in you

• 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

Location & Salary

• 100% remote option available within the Americas and Europe, with offices in SF, NYC, and Columbus
• Salary range between $150,000 - $250,000 based on skills and experience
• Significant equity stock options
• Medical, Dental and Vision Benefits
• Unlimited PTO
• Parental Leave separate from regular PTO policy
• 401k