Staff Engineer – Core Platform

About the Role

We’re looking for a Staff Engineer – Core Platform to architect, scale, and evolve the distributed systems foundation that powers Interface.ai’s next-generation AI experiences.

This is a hands-on, high-impact engineering role — you will design and build core platform components that enable real-time AI interactions, secure orchestration, and low-latency execution across millions of concurrent user sessions.

The ideal candidate is a systems thinker who thrives on solving large-scale engineering challenges in distributed, event-driven environments — someone who obsesses over performance, reliability, and elegant architecture, and who elevates the technical bar for the entire organization.

What You’ll Own

As a Staff Engineer, you will be the technical backbone for the Core Platform team — defining architecture, mentoring teams, and ensuring engineering excellence across all systems.
You’ll focus on:

• Designing and scaling low-latency, fault-tolerant distributed systems serving real-time workloads.
  
  
• Architecting microservices and event-driven systems that are secure, composable, and resilient under scale.
  
  
• Integrating Vector Databases and Embedding Stores to support intelligent retrieval, RAG (Retrieval-Augmented Generation), and adaptive AI experiences.
  
  
• Partnering with AI and Product teams to embed LLMs and inference services into the Core Platform, ensuring performance and observability.
  
  
• Defining technical standards, best practices, and evolutionary architecture patterns across teams.
  
  
• Driving continuous improvement in code quality, observability, and deployment reliability.
  
  
• Acting as a technical mentor and multiplier — raising the bar for system design, code reviews, and debugging excellence.
  
  

What You’ll Do

• Architect and Build Distributed Systems: Design microservice-based architectures that enable scalability, low latency, and fault isolation for AI-driven features.
  
  
• Optimize System Performance: Own performance at the platform level — from network I/O and API design to database indexing and caching strategies.
  
  
• Enable AI Integrations: Work closely with LLM engineers to design APIs and data pipelines supporting RAG, embeddings, and model-inference use cases.
  
  
• Design Resilient Data Infrastructure: Implement streaming and async systems (Kafka, Pulsar, or similar) to handle high-volume event traffic.
  
  
• Drive Engineering Quality: Establish patterns for clean code, contracts, testing, and documentation. Lead architecture and code reviews across pods.
  
  
• Mentor and Coach: Elevate senior engineers through structured mentorship, design walkthroughs, and technical guidance.
  
  
• Champion Evolutionary Architecture: Build for change — advocate for modular, observable, and testable systems that can evolve with business needs.
  
  
• Improve Platform Resilience: Implement retry, backoff, rate-limiting, and circuit-breaker patterns to ensure uptime and reliability at scale.
  
  
• Collaborate Cross-Functionally: Work with AI, data, DevOps, and product teams to define shared contracts, SLAs, and infrastructure standards.
  
  

What We’re Looking For

Required Qualifications

• Experience: 8+ years of experience in backend or platform engineering, including 2+ years in high-scale B2C or distributed systems environments.
  
  
• Distributed Systems Mastery: Deep understanding of scalability, consistency, concurrency control, and fault tolerance.
  
  
• Low-Latency Systems Expertise: Proven track record designing systems with strict SLA and sub-second response times.
  
  
• Microservices Architecture: Strong experience building, deploying, and maintaining service-oriented architectures with APIs, event streams, and async messaging.
  
  
• Vector DBs & Embeddings: Hands-on experience with Weaviate, Pinecone, Qdrant, FAISS, or similar; strong grasp of RAG patterns and semantic retrieval.
  
  
• Programming Proficiency: Expertise in Go, Rust, Java, or Python, and familiarity with modern frameworks (gRPC, GraphQL, REST).
  
  
• Data Layer Knowledge: Solid understanding of SQL/NoSQL databases (PostgreSQL, Cassandra, DynamoDB) and caching systems (Redis, Memcached).
  
  
• Resilience & Observability: Experience designing with telemetry, distributed tracing, chaos testing, and monitoring (Prometheus, OpenTelemetry).
  
  
• Engineering Quality Mindset: Passion for clean code, automated testing, CI/CD, and maintainability.
  
  
• Bar-Raising Leadership: Experience mentoring teams, enforcing code quality standards, and elevating design practices.
  
  

Preferred Qualifications

• Experience building or scaling real-time personalization or recommendation systems.
  
  
• Prior exposure to LLM serving, RAG pipelines, and LLMOps frameworks.
  
  
• Familiarity with Kafka, Flink, or Beam for data streaming.
  
  
• Contributions to open-source projects in distributed systems or AI tooling.
  
  
• Deep understanding of cloud-native architectures (Kubernetes, Istio, Terraform).
  
  

What Makes This Role Special

• You’ll define and scale the core technical foundation for AI systems serving millions of users.
  
  
• You’ll collaborate with world-class engineers across AI, platform, and product to deliver real-time, intelligent experiences.
  
  
• You’ll raise the engineering bar — shaping how code is written, reviewed, and deployed across teams.
  
  
• You’ll lead by example: mentoring senior engineers while remaining hands-on in architecture, design, and implementation.
  
  
• You’ll be part of an organization where AI-first thinking, evolutionary architecture, and engineering craftsmanship are core values.
  
  
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