Interface AI - Staff Engineer – Core Platform

• 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. • Distributed Systems Mastery: • scalability, consistency, concurrency control, and fault tolerance • Low-Latency Systems Expertise: Proven track record designing systems with strict SLA and sub-second response times. • Low-Latency Systems Expertise: • Microservices Architecture: Strong experience building, deploying, and maintaining service-oriented architectures with APIs, event streams, and async messaging. • Microservices Architecture: • Vector DBs & Embeddings: Hands-on experience with Weaviate, Pinecone, Qdrant, FAISS, or similar; strong grasp of RAG patterns and semantic retrieval. • Vector DBs & Embeddings: • Weaviate, Pinecone, Qdrant, FAISS • RAG patterns • semantic retrieval • Programming Proficiency: Expertise in Go, Rust, Java, or Python, and familiarity with modern frameworks (gRPC, GraphQL, REST). • Programming Proficiency: • Go, Rust, Java, or Python • Data Layer Knowledge: Solid understanding of SQL/NoSQL databases (PostgreSQL, Cassandra, DynamoDB) and caching systems (Redis, Memcached). • Data Layer Knowledge: • Resilience & Observability: Experience designing with telemetry, distributed tracing, chaos testing, and monitoring (Prometheus, OpenTelemetry). • Resilience & Observability: • Engineering Quality Mindset: Passion for clean code, automated testing, CI/CD, and maintainability. • Engineering Quality Mindset: • Bar-Raising Leadership: Experience mentoring teams, enforcing code quality standards, and elevating design practices. • Bar-Raising Leadership: • Experience building or scaling real-time personalization or recommendation systems. • real-time personalization or recommendation systems • Prior exposure to LLM serving, RAG pipelines, and LLMOps frameworks. • LLM serving, RAG pipelines, and LLMOps frameworks • Familiarity with Kafka, Flink, or Beam for data streaming. • Kafka • Flink • Contributions to open-source projects in distributed systems or AI tooling. • Deep understanding of cloud-native architectures (Kubernetes, Istio, Terraform). • 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. • core technical foundation • 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. • raise the engineering bar • You’ll lead by example: mentoring senior engineers while remaining hands-on in architecture, design, and implementation. • hands-on • You’ll be part of an organization where AI-first thinking, evolutionary architecture, and engineering craftsmanship are core values.#LI-Remote#LI-SS1 • AI-first thinking • evolutionary architecture • engineering craftsmanship • At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not  discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.