Staff Software Engineer, Batch and Realtime Streaming

Voleon is a technology company that applies state-of-the-art machine learning techniques to real-world problems in finance. As a Senior Software Engineer on the Batch and Realtime Streaming team, you will architect, develop, and maintain the Batch and Realtime Streaming platform that powers machine learning research and production trading systems.

Responsibilities:

• Architect, design, and implement core components for a Batch and Realtime Streaming platform, including data ingestion pipelines, storage systems, serving layers, and API interfaces
• Ship new features by collaborating across research, legal, trading, finance operations data, and infra teams for trading systems
• Collaborate with ML researchers and data scientists to understand their workflows and design intuitive interfaces (APIs, SDKs, UIs) for seamless feature discovery, access, and reuse
• Ensure data quality, consistency, and lineage for features, building robust mechanisms for versioning, monitoring, and governance
• Optimize data pipelines and storage for high performance, scalability, and reliability, considering both batch and real-time use cases
• Drive adoption of the feature store across teams by producing documentation, onboarding materials, and developer support
• Mentor junior engineers and contribute to team best practices and technical excellence

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent experience)
• 10+ years of software engineering experience, with a strong background in distributed computing and data infrastructure
• Experience in programming languages such as Python and Go
• Understanding of database technologies (Postgres, MySQL, Cassandra, DuckDB) and experience with APIs (REST/gRPC)
• Strong problem-solving skills, with a focus on delivering high-quality, maintainable, and well-documented solutions
• Excellent communication and collaboration skills; ability to work closely with both engineering and research teams
• Experience building large-scale data pipelines and storage systems (e.g., Airflow, Spark, Ray, Iceberg, etc)
• Exposure to modern Python data science tooling. (pandas, polars, dask, duckdb, etc)
• Experience with monitoring and observability tools for distributed systems (e.g., Prometheus, Grafana, ELK Stack)
• Prior experience working with feature stores (e.g., Feast, Hopsworks, or custom solutions) is highly desirable