Staff Software Engineer, Video
Flock SafetyFull Time
Senior (5 to 8 years)
Candidates must possess a Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent experience, along with 5+ years of software engineering experience focused on backend systems, distributed computing, or data infrastructure. Proficiency in programming languages such as Python, Go, and C++, understanding of database technologies (e.g., Postgres, MySQL, Cassandra, DynamoDB, SQLite, DuckDB, MongoDB), and experience with APIs (REST/gRPC) are essential. Strong problem-solving skills, a focus on delivering high-quality, maintainable, and well-documented solutions, and excellent communication and collaboration skills are also required. Preferred qualifications include experience building large-scale data pipelines and storage systems (e.g., Airflow, Spark) and exposure to modern Python data science tooling (pandas, polars, dask, duckdb).
The Senior/Staff Software Engineer will architect, design, and implement core components for a Batch and Realtime Streaming platform, including data ingestion pipelines, storage systems, serving layers, and API interfaces. They will ship new features by collaborating across research, legal, trading, finance operations data, and infra teams for trading systems. The engineer will also collaborate with ML researchers and data scientists to understand their workflows and design intuitive interfaces for seamless feature discovery, access, and reuse. Key duties include ensuring data quality, consistency, and lineage for features, building robust mechanisms for versioning, monitoring, and governance, and optimizing data pipelines and storage for high performance, scalability, and reliability. Additionally, they will drive adoption of the feature store across teams by producing documentation, onboarding materials, and developer support, and mentor junior engineers while contributing to team best practices and technical excellence.
Investment management using machine learning algorithms
Voleon focuses on investment management by utilizing machine learning to analyze financial market trends. The firm uses advanced statistical models to process large datasets and identify patterns that inform investment decisions, setting it apart from traditional methods that rely on human intuition. Voleon serves institutional clients and operates on a performance-based fee structure, aligning its interests with those of its clients. The company's goal is to provide data-driven insights that optimize investment returns while adapting to changing market conditions.