Senior Backend Engineer (Go)

About the role:

This is a fully remote role within New Zealand, Australia East Coast or nearby time zones.

Creative Fabrica is building a new generation of AI products, and we want the quality and speed of our creative output to match the ambition of the products themselves.

Our 26+ AI tools already serve hundreds of thousands of creators every day, from hobbyist crafters to professional designers. The engineering challenge is making heavy, long-running AI workloads feel fast and reliable at that scale.

We are looking for a Senior Backend Engineer with deep Go experience who can architect high-concurrency systems and take ownership of our multimodal generation pipeline. As a Senior Backend Engineer (Go), you will own the backend systems behind Studio AI's generation pipeline.

You will work closely with our other backend engineers, platform engineers, and Product to turn raw model output into a polished product experience.

This is not primarily an API-building role. The difficulty sits in orchestration: coordinating expensive inference across media domains, handling partial failure gracefully and keeping latency predictable under load.

You will have significant technical freedom. Sometimes you will pick up a defined problem; other times you will be given an objective and expected to define the architecture yourself.

Your overlap with our Amsterdam headquarters will mostly be asynchronous, so you should be comfortable working independently.

Our stack: Go, Kafka, SQS, gRPC, ConnectRPC, GraphQL, Postgres, AWS (EKS, S3, Lambda).

What you will do:

• Design and scale Go microservices that manage the full lifecycle of an AI generation.
• Optimise our ConnectRPC services and asynchronous pipelines (Kafka, SQS) to handle long-running, resource-intensive inference without compromising platform stability.
• Build the backend logic behind advanced features, including multimodal orchestration across images, video, audio, 3D and vector.
• Work with AWS to manage large data throughput and integrate with high-performance storage and compute clusters.
• Collaborate with external providers and Product to bridge the gap between model output and production user experience.
• Make and document architectural decisions on concurrency, queueing, backpressure and failure handling as the pipeline scales.
• Improve reliability and latency across the generation path, and own the observability needed to see both clearly.
• Own quality end to end: code review, testing strategy and on-call readiness for the services you build.
• Integrate new frontier models into production workflows quickly and safely.
• Contribute to technical direction across the wider backend platform, not only your own services.
• Stay current with distributed systems practice, AI infrastructure and model-serving developments.

What you will need: 

• Extensive experience building and scaling backend services in Go in high-traffic, high-volume SaaS environments.
• Proven ability to design distributed microservice architectures, including asynchronous processing (Kafka, SQS), low-latency service communication (gRPC, ConnectRPC or GraphQL) and long-running or resource-intensive workloads with queueing, backpressure and retry strategies.
• Deep AWS knowledge (EKS, S3, Lambda) alongside solid relational database skills, ideally Postgres: schema design, query performance and handling growth.
• Strong production discipline: observability, performance profiling, debugging live systems, code review and automated testing.
• Comfortable owning and documenting architectural decisions. Good written and spoken English.
• Strong sense of ownership over the reliability and quality of the systems you build, and over whether they actually perform for users rather than just read well.
• Pragmatic and self-directed; defines an approach without a fully specified brief, balances engineering quality against product goals, and ships dependable systems at pace rather than over-engineering.
• Strong written communicator who documents decisions by default, takes direct feedback well, and works effectively in a distributed team with limited real-time overlap.

Nice to have:

• Media processing experience such as video transcoding, audio synthesis or vector manipulation.
• AI infrastructure experience: model serving at scale, GPU-accelerated workloads, integrating third-party or open-source generative models into production, and cost optimisation for compute-heavy jobs.
• Background in a startup or other fast-moving, distributed environment, with an interest in helping shape product direction.