Senior Backend Engineer

Austin, United States | Posted on 05/27/2026

We are looking for a
Senior Backend Engineer
to help design, build, and support scalable backend systems for a modern data-driven software platform. This role is focused on production backend engineering, data architecture, API development, distributed processing, and operational reliability.

The ideal candidate has strong experience building backend services, working with large-scale data flows, improving database performance, and supporting systems in production. This person should be comfortable working on a product that includes modern AI-enabled features, including search, embeddings, model integrations, and related platform workflows.

In this role, you will help improve the backend platform that supports data ingestion, processing, APIs, search, and production application workflows.

Key areas of focus include:

• Building and maintaining reliable backend services
• Improving data ingestion and processing workflows
• Designing scalable database models, indexes, and query patterns
• Supporting API layers used by frontend and product teams
• Building and maintaining service-to-service data access patterns
• Improving system performance, reliability, and observability
• Supporting AI-adjacent workflows such as embeddings, vector-based search, provider integrations, evaluation hooks, and metadata management
• Helping improve infrastructure, deployment, testing, and operational practices

• Own backend services from design through deployment and production support
• Build and improve APIs with attention to performance, scalability, security, and maintainability
• Design and maintain backend service contracts and data access patterns
• Improve database performance through data modeling, indexing, aggregation/query optimization, and latency reduction
• Build reliable event-driven and asynchronous processing workflows
• Implement retries, idempotency, failure handling, dead-letter queues, rate limiting, and back pressure where needed
• Improve system observability through logging, metrics, tracing, dashboards, alerts, and runbooks
• Partner with product, frontend, design, and engineering teams to deliver features safely and efficiently
• Improve testing, code quality, CI/CD, and engineering standards
• Mentor other engineers and contribute to architecture and technical decision-making

• Strong experience building and maintaining backend services in production
• Strong experience with API development, including Graph
  
  QL or similar API patterns
• Experience with schema design, resolvers, authorization, pagination, batching, caching, and API performance optimization
• Strong experience with Mongo
  
  DB or similar document-oriented databases
• Experience with data modeling, indexing, aggregation pipelines, query optimization, and performance tuning
• Strong proficiency with Python for services, jobs, automation, data workflows, or internal tooling
• Experience with cloud-based backend systems, preferably AWS
• Experience with serverless, event-driven, or asynchronous architectures
• Experience with services such as queues, object storage, event buses, cloud functions, monitoring tools, and API gateways
• Experience with high-traffic, business-critical, or production-scale systems
• Solid understanding of backend performance patterns, including caching, async processing, connection management, and data access optimization
• Experience with Infrastructure as Code, preferably Terraform
• Experience with production testing strategies, including unit, integration, and contract testing
• Familiarity with CI/CD pipelines and modern deployment workflows
• Strong debugging, monitoring, incident response, and production support skills
• Comfortable working in a distributed team environment

Relevant experience may include:

• AWS or similar cloud platforms
• Serverless compute
• Messaging and event systems
• Object storage
• Monitoring and alerting tools
• API gateway patterns
• Infrastructure as Code
• IAM, networking, secrets management, and repeatable environments
• Private networking patterns
• Containerized workloads or Kubernetes-based environments

This role supports a platform with AI-enabled functionality. Candidates do not need to be machine learning researchers, but should understand how AI-powered software features are built, integrated, and supported in production.

• Embeddings and vector-based search concepts
• Integrating with external AI, LLM, or embedding providers
• Designing abstractions around third-party providers
• Handling timeouts, retries, fallbacks, and rate limits
• Understanding cost, latency, quality, and relevance tradeoffs
• Supporting evaluation, regression checks, observability, guardrails, and auditability
• Using AI developer tools to improve engineering speed, testing, quality, or maintainability

• Experience with very high-throughput backend systems
• Experience tuning concurrency, connection pools, and service latency
• Experience with search, analytics, saved views,…