Backend Engineer

Portcast is a venture-backed, Singapore-based logistics technology startup helping freight forwarders turn data into better decisions and measurable business impact.

Portcast uses AI to surface shipment exceptions, cost risks, and the right actions, helping teams focus on the shipments that need their attention, keep freight costs under control, and make procurement decisions with confidence. Our vision is to be the AI data and intelligence layer that powers exception management and margin improvement on every shipment.

Founded in 2018 and backed by leading technology investors, we are building for an industry at a critical inflection point of digital transformation. Our team of software engineers, data scientists, and logistics experts is on a mission to build the technology that helps freight forwarders protect margin, improve operational efficiency, and differentiate their offering.

ABOUT THE ROLE:

As our Backend Engineer, you will be reporting directly to our Engineering Manager and will work closely with our Product team to develop new products, scale existing ones and manage deployments and architecture. You won't just take tickets here.   You'll dig into how the system actually works, ask why before how, and chase a problem past the surface to the real cause. Curiosity isn't a nice-to-have on this team, it's how we build. 

WHAT YOU’LL OWN:

• Design, build and maintain APIs, services and systems and debug production issues across multiple levels of the stack. 

• Collaborate closely with our Business, Product and Data Science teams to understand and solve customer pain points at scale. 

• Design, Develop, Implement and Deploy enterprise level, high availability web application platforms, with support from the design team at Portcast.

• Create a technology infrastructure that functions well at scale. You will design and improve the system architecture.

• Own end-to-end the design AND implementation of how our product handles real-time data at scale, from event ingestion across dozens of carrier sources, through data normalization, through ML-powered risk and ETA models, to the user-visible recommendations our enterprise customers act on. 

• Build features customers actually use, harden internal tooling the team relies on, and create the alerting and monitoring surfaces that catch issues before customers do. You're a builder first, with the judgment to know what's worth building.

• Make architecture trade-offs explicit and defensible. 

• Instrument what you build, find real bottlenecks, and pick fixes that survive the next 10× of growth, not just the next sprint. You measure before you optimize, and your numbers are reproducible.