Principal Generalist Engineer

• Culture - We put our people first and prioritize the well-being of every team member. We've built a company where all opinions carry weight and where all voices are heard. We value and respect each other and always look out for one another. Above all, we are human.
• Learning - We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks.
• Compensation - You'll receive an attractive salary, pension, health insurance, paid leave plus other benefits.

We are hiring a senior Generalist Engineer for our Research & Development Team who can own problems end to end and deliver complete production systems, not just components. This is a broad, high-ownership role for an engineer who can operate across software, distributed systems, infrastructure, data, and, where useful, machine learning, moving from vague problem statements to clear designs, robust implementations, reliable deployment, and effective operation in production.

Success in this role requires strong first-principles thinking, sound technical judgment, deep debugging ability, and the willingness to work across boundaries to get the job done. We are looking for a true engineering generalist: someone who reduces complexity, improves reliability and performance, and treats ML as one tool among many rather than a default answer.

• Design and build production-grade systems that are reliable, scalable, and observable.
• Own systems end-to-end: problem → design → data → implementation → deployment → operations.
• Work across application services, distributed systems, infrastructure, data pipelines, and ML systems
• Debug complex production issues across multiple layers
• Make engineering trade-offs grounded in first principles
• Improve performance, latency, reliability, and cost efficiency
• Contribute to architecture and technical direction
• Write maintainable code and documentation
• Raise the engineering bar
• Machine Learning as Part of the Role
  • Frame problems correctly: when to use ML vs deterministic systems
  • Work with data end-to-end
  • Train, evaluate, and iterate on models
  • Build reproducible pipelines
  • Deploy models and monitor performance, drift, and cost
  • Debug system + model failures

• Strong CS fundamentals (DSA, OS, networking, distributed systems)
• Solid probability and statistics
• Experience building production systems at scale
• Ability to move across languages (Go, Java, Python, Rust, SQL)
• Understanding of system behavior under load and failure
• Comfort with Linux, containers, Kubernetes
• Strong debugging skills
• Ability to reason using invariants and failure modes
• Data systems, streaming systems, ML infrastructure, performance optimization

• Ownership over problems
• Simplicity over complexity
• Curiosity about internals
• Robust design thinking
• Clear communication

• Deliver working production systems from ambiguous problems
• Apply ML only when needed
• Build reliable, scalable systems
• Reduce complexity
• Earn trust in technical judgment

If you are a builder, a systems thinker, and a technologist who loves exploring what's next, we'd love to have you help shape the future of Moniepoint.