Senior Backend Engineer

Responsibilities

• Design and scale Go microservices managing the full lifecycle of AI generation.
• Optimize ConnectRPC services and asynchronous Kafka and SQS pipelines for long-running, resource-intensive inference.
• Build backend orchestration across image, video, audio, 3D, and vector generation.
• Use AWS to manage high data throughput and integrate with high-performance storage and compute clusters.
• Collaborate with external providers and Product to turn model output into production user experiences.
• Document architectural decisions involving concurrency, queueing, backpressure, and failure handling.
• Improve reliability and latency across the generation path and own observability.
• Own code review, testing strategy, and on-call readiness.
• Integrate new generative models into production workflows.
• Contribute to backend platform technical direction and stay current with distributed systems, AI infrastructure, and model serving.

Requirements

• Extensive experience building and scaling backend services in Go for high-traffic, high-volume SaaS environments.
• Experience designing distributed microservice architectures with asynchronous processing, low-latency service communication, queueing, backpressure, retry strategies, and resource-intensive workloads.
• Deep AWS knowledge, including EKS, S3, and Lambda.
• Solid relational database skills, preferably PostgreSQL, including schema design, query performance, and scaling.
• Production experience with observability, performance profiling, live-system debugging, code review, and automated testing.
• Ability to own and document architectural decisions.
• Strong written and spoken English communication skills.
• Self-directed approach, sound engineering judgment, and ownership of system reliability and quality.
• Ability to work 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, including model serving at scale, GPU-accelerated workloads, integrating generative models, and compute-cost optimization.
• Startup or fast-moving distributed-environment experience.