• Help build the next generation of AI-powered investigative products. • Join a small, senior team that combines application development and ML testing expertise. • Accelerate agent development, raise engineering standards, and turn work into a reusable AI platform. • Own ambiguous, high-impact technical problems end to end. • Influence architecture across teams and serve as a multiplier for engineers building AI-powered customer experiences.

• Architect and scale the pipelines that turn raw usage into accurate, real-time billing — metering, aggregation, rating, and invoicing across millions of events, from ingestion in ClickHouse to the rating engine. • Build payment flows that are correct under concurrency and partial failure: idempotent charges, retries, reconciliation, and clean handling of provider edge cases (Stripe and beyond). • Develop fraud and abuse detection — signal collection, real-time scoring, automated mitigation — that protects platform margin without getting in legitimate users' way. • Scale the systems everything else depends on: Postgres under heavy write load, Node.js services under pressure, and long-running workflows orchestrated with Temporal where exactly-once semantics and durability actually matter. • Build TypeScript + GraphQL APIs where correctness and auditability are non-negotiable. • Write Engineering Requirement Documents to take something from idea, to defined tasks, to implementation, to monitoring its success and scaling it further. • Contribute to our open-source repositories (CLI, Typescript SDK, Railpack, etc.) — Rust experience, or the desire to learn it, helps here. • Be oncall from time to time.

• Lead technical initiatives to evolve the platform with a focus on reliability, performance, and scalability • Develop and enhance services, integrations, and automations related to financial and operational flows • Work on development and maintenance of backend applications and system integrations • Design resilient solutions prepared for continuous operational growth • Improve architecture, technical patterns, and platform engineering best practices • Ensure observability, monitoring, and reliability of services and applications • Automate operational processes, deployments, and technical routines • Collaborate closely with Product, Operations, and Engineering teams

• Develop software using AI-assisted coding tools and modern ADLC practices, including SDD, while coaching team members on adopting AI-driven development practices • Lead by example in fostering a high-contribution culture through ownership, strong execution, effective communication, and productivity • Participate in code reviews with your peers • Ensure code quality and security while respecting development standards • Monitor features in production, identify potential issues, assess risks, and take the necessary actions.