Staff Software Engineer, Applied AI (Remote, US)

Sayari is a venture-backed global corporate data provider serving various sectors. They are looking for a Staff Software Engineer to build their platform, focusing on corporate transparency and combating financial fraud through innovative technology and engineering leadership.

Responsibilities:

• Architect classical and AI powered solutions that are robust, scalable, and thoroughly tested. Contribute directly to expanding the functionality and performance of our code base
• Collaborate with our Product and Design teams to evolve our platform roadmap, ensuring product goals are translated into tractable features that appropriately reflect our application's data model and capabilities. Identify capacity constraints to inform development sequencing and prioritization
• Lead technical architecture decisions for AI features, balancing research ambitions with production reliability
• Establish evaluation frameworks and best practices for working with large language models in high-stakes, accuracy-critical domains
• Act as a multiplier for the engineering team through hands-on mentoring, deep code reviews, and technical training on AI skillsets

Requirements:

• 10+ years of industry experience in software development, including experience as an IC and a mentor/lead
• Hands-on experience building production applications with LLMs (Prompt engineering, RAG, fine-tuning, or agentic workflows)
• Deep understanding and proficiency with SQL and NoSQL databases, including Postgres, Cassandra, Elasticsearch, and/or Cypher-based graph databases
• Proficiency in TypeScript and backend web development (REST APIs)
• Experience managing software in cloud environments (GCP, AWS, or Azure)
• Experience conducting code reviews and unit testing
• Experience with Graph databases and query optimization for large-scale data
• Experience working on both front end and back end code bases
• Experience with container orchestration via Kubernetes
• Interest in data visualization, or how to visually present large, complex datasets
• Exposure to batch processing frameworks like Spark