Senior/Staff Backend Engineer

• Design and build a purpose-built database for conversational AI data, including storage architecture, indexing, ingestion, query paths, and retrieval at scale. • Scale simulation infrastructure from approximately 30K toward 200K+ concurrent voice calls. • Architect systems for high availability, graceful degradation, and 99.99% uptime. • Own critical backend services built primarily with TypeScript/Node.js and Python. • Work with technologies across real-time voice and AI infrastructure, including LiveKit, Temporal, STT/TTS systems, and LLM tooling. • Build reliable pipelines for telephony events, recordings, transcripts, evaluations, and call outcomes. • Improve distributed tracing and production observability using technologies such as OpenTelemetry and SigNoz. • Diagnose and eliminate performance bottlenecks across high-throughput distributed systems. • Make architectural decisions around scalability, reliability, latency, storage, and infrastructure costs. • Ship quickly, measure production behavior, and continuously iterate. The engineering team currently deploys to production multiple times per day.

• 5+ years of software engineering experience, with strong backend or infrastructure expertise. • Proven experience building a database, queue, storage engine, streaming system, distributed data platform, or comparable large-scale infrastructure. • Ideally, you were a primary author, architect, or technical owner rather than simply a user or contributor. • Strong knowledge of distributed systems, including partitioning, replication, concurrency, consistency, failure recovery, backpressure, and graceful degradation. • Experience scaling infrastructure under significant real-world production load. • Ability to discuss previous systems quantitatively — for example, requests/events per second, concurrency, p95/p99 latency, data volumes, availability, cluster size, or cost improvements. • Strong backend development experience. TypeScript/Node.js and Python are used on the project, but engineers from other strong infrastructure backgrounds are welcome. • Strong understanding of production monitoring, observability, and distributed tracing. • High level of ownership and ability to operate effectively in a fast-moving environment. • AI-native mindset: actively using modern AI tools to accelerate engineering, debugging, research, and development. • Strong bias to action — comfortable building the smallest working solution, shipping it, learning from production, and iterating.

Location: Remote — US working hours overlap Engagement: Full-time / Long-term project Compensation: $160K–$240K annually + meaningful equity Seniority: Senior / Staff About the Project We are looking for a Senior/Staff Software Engineer to join a rapidly growing technology company building infrastructure for AI-powered voice agents. The product helps companies test and improve their AI voice agents at scale. The platform can automatically run thousands of simulated calls using different accents, tones, speaking styles, personalities, and scenarios, then analyze the conversations and identify bugs, failures, and unexpected behavior. The company is experiencing rapid growth and is now scaling its infrastructure from approximately 30K concurrent voice calls toward 100K–200K+ concurrent calls. A major upcoming initiative is the development of a purpose-built database for conversational AI, designed to efficiently store, process, query, and retrieve massive volumes of voice-agent interaction data. We're looking for someone who has genuinely built data infrastructure at scale — ideally as a primary author, architect, or technical owner of a database, queue, storage system, streaming platform, or other large-scale data system. What You'll Do • Design and build a purpose-built database for conversational AI data, including storage architecture, indexing, ingestion, query paths, and retrieval at scale. • Scale simulation infrastructure from approximately 30K toward 200K+ concurrent voice calls. • Architect systems for high availability, graceful degradation, and 99.99% uptime. • Own critical backend services built primarily with TypeScript/Node.js and Python. • Work with technologies across real-time voice and AI infrastructure, including LiveKit, Temporal, STT/TTS systems, and LLM tooling. • Build reliable pipelines for telephony events, recordings, transcripts, evaluations, and call outcomes. • Improve distributed tracing and production observability using technologies such as OpenTelemetry and SigNoz. • Diagnose and eliminate performance bottlenecks across high-throughput distributed systems. • Make architectural decisions around scalability, reliability, latency, storage, and infrastructure costs. • Ship quickly, measure production behavior, and continuously iterate. The engineering team currently deploys to production multiple times per day. What We're Looking For • 5+ years of software engineering experience, with strong backend or infrastructure expertise. • Proven experience building a database, queue, storage engine, streaming system, distributed data platform, or comparable large-scale infrastructure. • Ideally, you were a primary author, architect, or technical owner rather than simply a user or contributor. • Strong knowledge of distributed systems, including partitioning, replication, concurrency, consistency, failure recovery, backpressure, and graceful degradation. • Experience scaling infrastructure under significant real-world production load. • Ability to discuss previous systems quantitatively — for example, requests/events per second, concurrency, p95/p99 latency, data volumes, availability, cluster size, or cost improvements. • Strong backend development experience. TypeScript/Node.js and Python are used on the project, but engineers from other strong infrastructure backgrounds are welcome. • Strong understanding of production monitoring, observability, and distributed tracing. • High level of ownership and ability to operate effectively in a fast-moving environment. • AI-native mindset: actively using modern AI tools to accelerate engineering, debugging, research, and development. • Strong bias to action — comfortable building the smallest working solution, shipping it, learning from production, and iterating. Nice to Have • Database internals / storage engine development • Distributed databases • Kafka, Pulsar, Redpanda, or similar messaging infrastructure • Streaming or high-throughput ingestion systems • Query engines and indexing • Observability infrastructure • OpenTelemetry / SigNoz • WebRTC / LiveKit • Telephony, SIP, or RTP • Voice or audio processing • STT/TTS infrastructure • LLM infrastructure • Experience at a database, data infrastructure, observability, or developer infrastructure company Recruitment Process 1. Short Screening Interview A brief AI interview (recommended for faster candidate submission) or conversation with a recruitment agency representative to discuss the candidate's background, relevant experience, and fit for the role. 2. Candidate Submission The candidate's profile is submitted to the client. We'll notify the candidate once the hiring team confirms whether they would like to proceed. 3. Intro Call — 15 minutes A brief conversation with a team member to assess culture fit, interest in systems/infrastructure work, ownership mindset, and alignment with a fast-paced engineering environment. 4. Coding Interview — 30 minutes An open-book live coding session. Candidates are encouraged to use their preferred AI tools while building something in real time. The focus is on coding fluency, speed of prototyping, problem-solving, and an AI-native engineering workflow. 5. Systems Design Interview #1 — 60 minutes Focused on distributed systems, database architecture, and scalability. The discussion may cover technologies and concepts related to PostgreSQL, Redis, Kafka, and high-scale real-time infrastructure. 6. Systems Design Interview #2 — 60 minutes A second technical design interview with another engineer, covering a different problem area such as observability, voice simulation infrastructure, data pipelines, or large-scale distributed systems. 7. Paid Work Trial — 2–3 days A paid trial based on a real engineering problem. This gives both sides an opportunity to evaluate delivery speed, technical decision-making, communication, collaboration, and overall fit. 8. Offer & Hiring Successful candidates