Backend & ML Engineer - Social Feed Architecture

We are building a next-generation AI-driven social platform combining high-engagement short-form video feeds with intelligent product recommendations and discovery., * Architect & Scale Feed Infrastructure: Design, build, and optimize our high-throughput social feed delivery pipeline (fan-out services, caching strategies, data aggregation, and low-latency API serving).

• Power ML & Recommendation Pipelines: Implement ranking algorithms, personalized recommendation pipelines, and content filtering using modern ML approaches (e.g., candidate generation, two-tower models, collaborative filtering).
• Integrate AI/Multimodal Tools: Leverage state-of-the-art AI tooling (vector search, vision-language models, and LLM APIs) to automatically extract metadata, tag video content, and personalize user feeds.
• AWS Cloud Performance: Manage, scale, and optimize cloud services on AWS to guarantee microsecond-to-millisecond response times under peak user loads.
• Cross-Functional Collaboration: Partner directly with the founder and mobile engineering team (React Native/Expo) to ensure seamless API integration and smooth 60fps feed rendering on the client side.

• 5+ years of Senior Backend Engineering experience using Python (FastAPI, AsyncIO, PyTorch, Django, or Flask).
• Proven Domain Expertise: Direct experience building or scaling social feeds, recommendation engines, or discovery algorithms (e.g., experience from Snap, TikTok, Tinder, Meta, Pinterest, or high-scale social platforms).
• AWS Cloud Expertise: Hands-on experience with core AWS services (ECS/EKS, Lambda, ElastiCache/Redis, DynamoDB, OpenSearch, S3, SageMaker/Bedrock).
• Modern AI / Vector Tooling: Familiarity with vector databases (Pinecone, Weaviate, Qdrant, or OpenSearch Vector Search) and embedding models for semantic retrieval and recommendation.
• Data & System Architecture: Strong mastery of caching strategies (Redis/Memcached), asynchronous processing (Celery/Kafka/SQS), relational & NoSQL databases, and graph database concepts.

Bonus Points

• Background in Computer Vision / Multimodal AI integration for automated video tagging and product recognition.
• Experience with real-time analytics, event-driven architectures, or A/B testing infrastructure for feed ranking models.
• Prior experience working in fast-moving startup environments.