Senior/Staff Backend Engineer

• →Design and implement storage structures, query paths, and retrieval systems that operate efficiently at scale.
• →Scale simulation infrastructure from 30K to 200K+ concurrent calls while architecting for graceful degradation and 99.99% uptime.
• →Own core backend services using TypeScript/Node.js and Python.
• →Build backend systems that orchestrate LiveKit, Temporal, STT/TTS, and LLM tooling for real-time voice agents.
• →Harden production monitoring pipelines so telephony events, recordings, and outcomes propagate reliably across services at massive scale.
• →Deepen observability using OpenTelemetry, SigNoz, and trace-first practices.
• →Improve system visibility so production issues and system behavior can be understood quickly and efficiently.
• →Design and build infrastructure capable of handling high-volume, real-time workloads.
• →Identify system bottlenecks, investigate performance issues, and implement scalable solutions.
• →Ship the smallest working element quickly, learn from production usage, and iterate continuously.
• →Work in an engineering environment that deploys to production multiple times per day.
• →Contribute to infrastructure and backend architecture decisions as the company scales.
• →Take ownership of complex technical problems and drive solutions from implementation through production.

Required Qualifications

• →Minimum 5 to 15 years of experience in backend/infrastructure engineering, with strong database and distributed systems experience.
• →Proven experience building or being a core contributor to the storage or query layer of a database, queue, or large-scale data system, ideally within the last three years.
• →Deep expertise in database technologies such as Postgres, Redis, Kafka, or ClickHouse.
• →Experience scaling distributed systems under real production workloads, including GB-to-TB per day, high cardinality, tight SLAs, and clearly defined performance bottlenecks.
• →Proficiency in TypeScript/Node.js.
• →Experience with cloud-native infrastructure, including AWS, Terraform, and Kubernetes.
• →Experience with workflow engines such as Temporal or Cadence.
• →Experience building database and distributed infrastructure systems at scale.
• →Strong understanding of systems design and infrastructure engineering.
• →Experience working at a VC-backed startup, preferably Series A or later.
• →Strong ability to operate with a high degree of ownership and autonomy.
• →Demonstrated ability to prototype quickly, iterate, and ship production solutions within days rather than months.
• →AI-native engineering approach, with daily experience using AI-assisted development tools such as Cursor, Claude Code, or Codex.
• →Ability to form informed opinions about LLM capabilities and tradeoffs and determine when different AI tools or approaches should be used.
• →Strong passion for and mastery of systems design and infrastructure.
• →Must be able to work during U.S. working hours.
• →U.S. Citizen or Green Card Holder required.

Preferred Qualifications

• →Experience at an observability, data infrastructure, or database company, such as Datadog, Temporal, ClickHouse, or Stripe.
• →Experience as a founding engineer or early employee at a startup.
• →Experience building real-time voice, audio, or telephony pipelines.
• →Background in Scala, Rust, or Haskell, demonstrating systems-level engineering experience.
• →Open-source contributions or personal projects involving systems or databases.

Ideal Candidate Profile
The ideal candidate is a senior infrastructure or backend engineer who has worked deeply on databases, queues, distributed systems, or other large-scale data infrastructure. This person should be comfortable working close to the storage and query layers and solving difficult scalability, reliability, and performance problems under real production load.

The candidate should have a strong bias toward action, be comfortable building and shipping quickly, and have experience working in a high-growth startup environment with significant ownership and autonomy.

The role is particularly suited to an engineer who is AI-native, actively uses AI-assisted coding tools, and has thoughtful perspectives on how and when to use LLM capabilities in software development.

Candidates should be comfortable working in a fast-moving startup environment where engineers are expected to build rather than remain limited to narrow responsibilities or extended planning cycles