About TrueBiz
TrueBiz is a YC-backed merchant risk intelligence platform. We deliver 250+ risk signals on any business in under 30 seconds via API.
Our customers include some of the largest payment networks, fintechs, banks, and merchant services companies in the world. We help risk and underwriting teams understand who a merchant is, what they do, and whether their online presence suggests fraud, compliance, or business model risk.
We are a lean, profitable team building critical infrastructure for the payments ecosystem.
We use AI heavily, both inside the product and in how we build it. Our product uses LLMs as part of broader Python-driven workflows for analyzing businesses, websites, online presence, and risk signals. Our engineering team uses AI-assisted development tools aggressively to write, review, debug, refactor, and reason about code. We do not think of ourselves as an “AI wrapper” company, but we do expect engineers to have embraced modern AI tools and to use them with strong production judgment.
Why This Role Exists
We’re looking for a senior engineer to support our scale. You’ll strengthen the infrastructure behind a live, revenue-generating product as we support larger customers, higher usage, and more demanding enterprise requirements: AWS, ECS/Fargate, CloudFormation, CI/CD, release management, observability, and reliability.
But this isn’t a pure platform role. You’ll also ship product features, build APIs, improve backend workflows, and work across the full stack. We need pragmatic builders who can improve systems while continuing to ship.
This is a core team role. The work may skew more toward backend/platform, product engineering, or applied AI systems depending on your strengths, but we are looking for someone who can operate broadly and take ownership across the system.
What You'll Do
- Own and evolve our AWS infrastructure: ECS/Fargate, RDS, ElastiCache, S3
- Build and maintain CI/CD pipelines, deployment automation, and release management processes
- Improve observability, alerting, and incident response as we scale
- Ship full-stack product features in Python/Django and React
- Improve the reliability, latency, and debuggability of workflows that depend on external APIs, web data, and LLM calls
- Use AI-assisted development tools to move faster on implementation, debugging, test generation, refactoring, documentation, and code review
- Work on both new systems and unglamorous but important fixes: bugs, edge cases, small optimizations, operational cleanup, and customer-impacting issues
- Collaborate with engineering, sales, and customer success to support enterprise customers
What We're Looking For
- Professional software engineering experience building, shipping, and maintaining production systems. We care more about demonstrated ownership and judgment than years of experience, but this is not an entry-level role.
- Ideally, experience at a fast-growing startup or small engineering team where you had to ship quickly, operate with ambiguity, and own work beyond a narrow job description.
- Strong in Python, Django, and PostgreSQL
- Comfortable owning production AWS infrastructure end-to-end, from deployment configuration and permissions to reliability, scaling, observability, and cost management
- Comfortable with React: you don't need to be a frontend specialist, but you can build and modify UI when needed
- Proficient with AI-assisted development tools such as Claude Code, Cursor, Copilot, or similar tools. You use them regularly and they make you measurably faster.
- Strong judgment when working with AI-generated code: you know how to review it, test it, constrain it, and keep it from making the codebase worse.
- Git: clean branches, good commit hygiene, comfortable with rebases and release workflows
- Bias toward action. You default to shipping over deliberating, but you know when infrastructure decisions need to be right the first time.
- Comfortable in a small team where the work ranges from architecture decisions to customer-impacting bugs
- Comfortable with some customer context. This is not a sales role, but you should care about how engineering decisions affect real enterprise users.
Nice to Have
- Experience with ECS/Fargate, Terraform or other IaC tools, CI/CD, and production deployment workflows
- Experience with observability tools such as Datadog, OpenTelemetry, CloudWatch, Grafana, or similar
- Experience with API Gateway or production API infrastructure
- Experience with queue-based or async workflow systems such as Celery
- Experience operating systems that depend on third-party APIs, scraping, external data vendors, or LLM APIs
- Experience with payments, fintech, fraud, underwriting, compliance, or risk systems
What Success Looks Like
- Run on a more reliable, observable, and scalable AWS foundation.
- Ship with safer and more predictable deployment workflows.
- Improve uptime, latency, alerting, and operational confidence for enterprise customers.
- Reduce infrastructure fragility and manual operational work.
- Continue shipping customer-facing product improvements while strengthening the platform underneath.
- Make AI-assisted development a force multiplier without sacrificing code quality, maintainability, or production reliability.