M13

Compensation

Full Description

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. What You'll Do Backend & Infra Ownership You'll work and add to our C# / .NET / ASP.NET backend api layer (have experience in this or something similar like Java/Spring and can learn quickly) and progressively add many Python microservices (FastAPI or AWS Lambda) with modular, AI-native architecture in mind to build our intelligence layer. Deploy models and setup 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 of your 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. AI & ML Systems Integration 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.). Cloud Infra, DevOps, and Data Stack Manage our AWS infrastructure and add to our current stack with new innovate technologies: Lambda, ECS, S3, RDS (Postgres), CloudFront, IAM, Route53 — you’ll be the one making the call on architecture and trade-offs. Be able to use search databases like OpenSearch Infra-as-code with Terraform. Pipelines through GitHub Actions. 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 of your 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. Personalization & Emotional Intelligence Layer 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. All personalization logic You’re Probably Right for This If: You have 6 - 15 years of experience in backend or full-stack development; building 0-1 products or teams in a startup environment You’ve shipped entire production backends at high-growth early-stage startups — you know how to move fast and still write code you don’t hate six months later. You’ve integrated or scaled LLM-based products — bonus if you’ve done it with emotion, memory, or personalization layers. You care about systems thinking, fast response times, clean abstractions, and building infra that won’t fall over under load. You’ve done the zero-to-one and can hold both the product in your head and the infra in your hands. You’re able to figure things out quickly and dive in wherever needed. There’s no “that’s not my job” here. You're allergic to bloated teams — you’d rather build it right yourself than manage a swarm of mid devs doing it wrong.