Backend ML Engineer at Robyn AI

M13-posted 2 months ago
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Robyn is not just an AI app — she’s your emotionally intelligent companion. A trusted mirror. A guide. A new kind of OS for your emotional life. We're building the world’s first emotional intelligence layer for AI. You’ll be building the backend infrastructure that powers all of it: Conversations, memory, and real-time personalization, Voice + chat interface, Scalable infra for emotional intelligence, Secure and fast APIs for our iOS app, A robust ML inference and fine-tuning pipeline. You’ll be the technical backbone of Robyn — designing and shipping fast, scalable, emotionally aware systems while collaborating closely with iOS, product, and AI teams. This is a rare opportunity to define the foundations of emotionally intelligent AI. Everything beyond the core LLM — memory, emotional layer, and relational engine — is built fully in-house. The backend engineer will help architect the systems that make Robyn feel human: writing the foundational codebase for the next wave of AI — one that feels, remembers, and connects.

  • Work and add to our C# / .NET / ASP.NET backend API layer and progressively add many Python microservices (FastAPI or AWS Lambda) with modular, AI-native architecture in mind.
  • Deploy models and set up some ML training pipelines.
  • Understand dependency injection, Strategy Pattern, inversion of control, and other best practices for code maintainability.
  • Own the full backend surface area — auth, APIs, infra, orchestration — and design all features for scale and velocity.
  • Build and maintain REST and GraphQL APIs consumed by our iOS client; low-latency, resilient, and well-instrumented.
  • Architect a microservice-style ML model serving backend deployed via Docker containers or AWS Lambda (SnapStart), backed by async eventing and pub/sub where needed.
  • Own CI/CD, rollback strategies, logging, error handling — the backend is your domain, end-to-end.
  • Architect and manage existing vector DB (PgVector) and potentially add more to power retrieval-augmented generation, evolving memory, and personalization.
  • Build tools and add to our custom memory pipelines tied to user context, embeddings, and interaction history.
  • Integrate and scale inference with OpenAI, Claude, Llama, or other models. Build wrappers, manage caching, fallbacks, and prompt routing logic.
  • Own emotion and sentiment tagging workflows; plug in APIs or run lightweight classifiers in-line.
  • Maintain API orchestration layer with 3rd party model providers (OpenAI, ElevenLabs, etc.).
  • Manage our AWS infrastructure and add to our current stack with new innovative technologies: Lambda, ECS, S3, RDS (Postgres), CloudFront, IAM, Route53.
  • Use search databases like OpenSearch.
  • Implement infra-as-code with Terraform and pipelines through GitHub Actions.
  • Ensure full observability: metrics, structured logging, tracing, alerting — Open Telemetry, Sentry, Grafana, Cloudwatch, etc.
  • Optimize latency across API surface, tune Postgres indexes, add Redis caching layers to many services and pub/sub or streaming for near-instantaneous data sync.
  • Set up and secure infra for SOC-2 readiness: access controls, data lifecycle policies, encrypted storage.
  • Design and ship emotion-aware backend systems that update in real time based on user behavior.
  • Implement custom memory engines — user embeddings, experience graphs, emotional scores — and build APIs that adapt over time.
  • Work with product and AI to tune behavior of Robyn based on user feedback, emotion logs, memory history, and interaction loops.
  • 6 - 15 years of experience in backend or full-stack development.
  • Experience building 0-1 products or teams in a startup environment.
  • Experience shipping entire production backends at high-growth early-stage startups.
  • Experience integrating or scaling LLM-based products, especially with emotion, memory, or personalization layers.
  • Strong understanding of systems thinking, fast response times, clean abstractions, and building infra that won’t fall over under load.
  • Ability to hold both the product in your head and the infra in your hands.
  • Quick problem-solving skills and willingness to dive in wherever needed.
  • Experience with emotion and sentiment tagging workflows.
  • Familiarity with AWS services and infrastructure management.
  • Experience with CI/CD processes and tools.
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