Backend engineer (Go)

About the job

You’ll work at the core of our production platform, building and operating backend services that power APIs, product features, and ML inference systems.

This is a mid-level backend role focused on reliable service delivery, API quality, and production ML-serving infrastructure. You’ll primarily build in Go, contribute occasionally in Rust or Python, and support cloud-native systems running mostly on AWS (with some GCP).

You won’t be doing ML research — but you will be responsible for making trained models work reliably in real products.

Your key responsibilities

• Design, implement, and maintain backend services that power customer-facing and internal capabilities.
• Create and maintain APIs with strong contracts, documentation, versioning, and backward compatibility practices.
• Integrate trained model artifacts into backend services and release workflows. Build and operate inference endpoints and supporting systems such as model routing, versioning, caching, and feature retrieval.
• Monitor and optimize latency, throughput, error rates, and infrastructure cost across backend and inference services.
• Write appropriate unit and integration tests, participate in code reviews, and follow team standards for documentation and style. Debug issues across environments and contribute to root-cause analysis and prevention.
• Deploy, monitor, and scale services primarily in AWS (and some GCP). Contribute to CI/CD workflows and release safety improvements.
• Add observability (metrics, logs, traces), assist with incident response, and follow secure engineering practices including proper auth patterns and secrets handling.
• Work closely with ML and data engineers on serving requirements, data contracts, and operational quality — focused on inference and serving, not training.

What makes this role different

• You won’t just build CRUD services — you’ll help run ML-powered systems in production
• You’ll work directly on inference infrastructure: model serving, routing, performance, and cost optimization
• You’ll contribute to both product APIs and intelligence-serving systems
• You’ll have meaningful ownership of production services and their reliability
• Startup mindset required — be ready to adapt, take ownership, and occasionally contribute outside your core lane (for example small SDK or dashboard support in TypeScript when needed)

Required qualifications

• 2–5 years (or equivalent) professional experience building backend systems in production
• Strong hands-on experience with Go, including concurrency, debugging, profiling, and performance-aware development
• Experience building and maintaining APIs consumed by other teams or customers
• Experience with relational and/or NoSQL databases and common data-access patterns
• Experience operating services in cloud environments, ideally AWS (compute, networking basics, IAM, monitoring)
• Practical familiarity with ML inference concepts such as model versioning, latency/throughput tradeoffs, and monitoring (no research background required)
• Hands-on experience operating ML inference services (for example PyTorch or ONNX), ideally in recommendation, semantic search, or personalization systems
• Strong engineering practices: testing, code review, documentation, and collaborative delivery

Preferred qualifications

• Exposure to Rust and/or Python in production settings
• Familiarity with distributed systems patterns (queues, streams, retries, idempotency, eventual consistency)
• Experience with containers, Kubernetes, and/or infrastructure-as-code (Terraform or CDK)
• Experience with ecommerce or event-driven product systems (catalog, personalization, analytics, experimentation)
• Familiarity with security and privacy practices in multi-tenant SaaS systems
• Experience contributing to SDKs or developer-facing tooling

This role is ideal for someone who

• Enjoys building production backend systems that are actually used by customers
• Cares about API quality, reliability, and operational excellence
• Likes performance tuning and debugging real systems
• Is interested in ML-powered products and inference systems, even without doing ML research
• Balances delivery speed with maintainability and clean design
• Works well in collaborative design and code review environments