Senior Software Engineer – AI Data Infrastructure

Job description

ABOUT THE ROLE Our client is an early-stage AI infrastructure company building the training data and evaluation systems used by leading frontier AI labs. The company designs high-signal datasets and rigorous evaluations that help improve the next generation of AI models. As a Senior Software Engineer, you will have end-to-end ownership of projects across full-stack and platform engineering. You will build human-in-the-loop platforms, fraud detection systems, scalable data infrastructure, and annotator workflows. You will work directly with leading AI labs and gain exposure to emerging model capabilities before they reach the broader market. As an early engineering team member, you will have significant influence over core systems, technical decisions, and product direction. KEY RESPONSIBILITIES - Design and build scalable systems, with a focus on the company's expert platform. - Own projects end-to-end, from technical design through implementation and deployment. - Build and improve infrastructure supporting AI training data and evaluation. - Develop human-in-the-loop platforms, annotator workflows, and related tooling. - Solve complex scalability, reliability, and distributed systems challenges. - Collaborate with research, product, and design to ship features quickly without sacrificing quality. - Write clean, maintainable code and contribute to technical documentation. - Participate in code reviews and technical design discussions. - Make pragmatic trade-offs between speed, simplicity, reliability, and maintainability. - Identify problems proactively and drive solutions independently. REQUIREMENTS - 5+ years of professional software engineering experience preferred. - Strong coding fundamentals in Node.js and TypeScript, Python, and/or Go. - Deep understanding of distributed systems, scalability, and system design. - Experience with cloud platforms such as AWS or GCP. - Familiarity with Kubernetes, APIs, and NoSQL databases. - Strong ability to reason through technical trade-offs and balance speed with reliability. - Active GitHub profile with real projects demonstrating a strong builder mindset. - Ability to work autonomously while collaborating effectively with a high-output engineering team. - Proactive approach to identifying problems and driving solutions without extensive direction. - High-accountability mindset and ability to perform effectively in a demanding startup environment. - Strong bias toward simple solutions to complex problems. - Strong written and verbal communication skills. - Ability to clearly articulate why you want to join an early-stage startup and work in AI now. PREFERRED QUALIFICATIONS - Experience as a founding engineer at a high-growth startup. - Experience founding and building a technically sophisticated product. - Previous success in fast-paced startup environments. - Experience building infrastructure or platforms for AI, data, evaluation, or human-in-the-loop workflows. COMPENSATION & LOGISTICS - Compensation: $84,000–$120,000 USD annually - Equity: Competitive equity package - Work Setup: Remote from Latin America WHAT SUCCESS LOOKS LIKE IN YOUR FIRST 3–6 MONTHS - Take ownership of meaningful engineering projects and ship them from design through production. - Become a reliable contributor to the core systems supporting AI training data and evaluation. - Improve the scalability and reliability of key platform components. - Ship product and infrastructure improvements while maintaining a high engineering standard. - Build strong working relationships across engineering, research, product, and design. - Proactively identify technical problems and drive them toward practical solutions. COMPANY OVERVIEW Our client is a rapidly growing AI infrastructure company building systems that help leading AI labs create training data and evaluate model performance. The company is an early-stage, post-Series A organization with a small engineering team and significant individual ownership. Engineers have the opportunity to work on technically challenging problems and directly influence the systems powering the next generation of AI.