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Senior Backend Engineer

  • Department: Engineering

  • Reports to: CTO

  • Work location: On-site/Hybrid

Position summary

Senior Backend Engineer designs, implements, and maintains backend services that power our products.

This role is primarily focused on backend delivery in Go, with occasional contributions in Rust or Python, and limited support for TypeScript SDK/dashboards when necessary.

The position contributes to service reliability, API quality, and cloud operations (primarily AWS, some GCP), operating with moderate autonomy.

The role supports ML inference and ML-serving infrastructure - integrating trained models into production systems, operating inference services reliably, and optimizing latency/cost - without performing ML research or model development.

Essential duties and responsibilities

Backend development

  • Implement and maintain production backend services primarily in Go, delivering customer-facing and internal capabilities.

  • Develop and evolve APIs, including documentation, versioning, and backward compatibility practices.

  • Build integration points as they relate to backend services.

ML inference & serving infrastructure (non-research)

  • Productionize trained models by integrating model artifacts into backend/inference services and release workflows.

  • Build and operate inference endpoints and supporting systems (e.g., model routing/versioning, caching, feature retrieval, request/response schemas).

  • Monitor and improve inference reliability and performance (latency, throughput, error rates), and contribute to cost optimization.

  • Collaborate with ML/data teammates on operational requirements (input features, data contracts, quality checks), focusing on serving, not training.

Quality and maintainability

  • Write tests appropriate to the change (unit/integration), participate in code reviews, and follow team standards for style and documentation.

  • Debug and resolve defects across environments; contribute to root-cause analysis and preventative improvements.

  • Make incremental improvements to system design and codebase health (refactoring, reducing tech debt, improving developer experience).

Reliability and operational readiness

  • Add and maintain observability (logging, metrics, tracing) for owned components.

  • Support deployments and production operations, assist with incident response when needed.

  • Follow secure engineering practices (authentication/authorization patterns, secrets handling, least privilege access).

Cloud and delivery

  • Contribute to build/release processes and CI/CD workflows; improve release safety.

  • Work with AWS services (and some GCP) to deploy, monitor, and scale services; participate in cost/performance optimization efforts.

Non-essential duties

  • Participate in customer technical discussions for integration troubleshooting or implementation guidance as needed.

  • Contribute to internal tools and documentation that improve engineering efficiency.

  • Contribute to SDKs and customer facing dashboards as needed.

Scope and decision-making authority

  • Owns implementation and quality for assigned components/services, including design within established patterns.

  • Proposes improvements to existing systems and participates in technical design discussions led by senior/staff engineers.

  • Makes day-to-day technical decisions within team conventions; escalates cross-team architecture decisions as appropriate.

Required qualifications

  • 5 years (or equivalent) professional experience building backend systems in production.

  • Practical experience in Go and experience with concurrency, profiling/debugging, and performance-minded development.

  • Experience building and maintaining APIs consumed by other teams or customers.

  • Experience with relational and/or NoSQL databases and common data-access patterns.

  • Familiarity operating services in cloud environments, ideally AWS (compute, networking basics, IAM, monitoring).

  • Familiarity with ML inference concepts (model versioning, latency/throughput tradeoffs, monitoring), no research background required.

  • Hands-on experience operating ML inference services (PyTorch, ONNX), ideally recommendation engines, semantic search, personalization.

  • 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 infrastructure-as-code (Terraform/CDK) and/or containers/Kubernetes.

  • Experience with ecommerce concepts (events, catalog, personalization) or analytics/experimentation systems.

  • Familiarity with security/privacy practices in multi-tenant SaaS.

Performance measures

  • Delivers backend features that meet functional requirements and agreed timelines.

  • Improves service robustness via testing and observability; reduces repeat defects.

  • Maintains reliable APIs (compatibility, correctness, documented behavior).

  • Demonstrates effective collaboration and code review participation.

  • Contributes to maintainability and operational readiness of owned services (deployability, monitoring, safe changes).

Tools and technologies (current environment)

  • Languages: Go (primary), Rust, Python; TypeScript (limited)

  • Cloud: AWS (primary), GCP (secondary)

  • Interfaces & infrastructure: REST, CI/CD, monitoring/observability, databases, queues/streams

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