Senior Product Engineer
5 hours ago
About Nova:
Nova is building AI teammates that help businesses protect revenue, prevent invisible losses, and free teams from repetitive work. We focus on high-impact areas like reconciliations, contract reviews, and compliance tracking; turning manual tasks into ROI-driven automation that empowers people to do their best work.
About the role:
We're looking for a Product Engineer III – Backend who is outstanding at building highly scalable, blazing-fast APIs. You'll be responsible for designing and implementing backend systems that serve as the foundation for Nova's AI teammates. This role demands an architectural mindset: designing APIs and services that aren't just functional, but performant, resilient, and built to handle massive scale.
While Node.js/TypeScript is our core stack, Python experience is a strong advantage as Nova's ML components increasingly interact with backend systems.
What you will work on:
- Design and build scalable APIs in Node.js/TypeScript (Fastify / NestJS / Express) with performance at the core.
- Model and optimize data in PostgreSQL
- Implement queuing and streaming systems (Kafka, NATS, RabbitMQ, or equivalents) to enable reliable event-driven workflows.
- Introduce caching systems (Redis, Memcached, or similar) to deliver low-latency, high-throughput APIs.
- Drive API architecture for high-scale use cases — multi-tenant workloads, real-time processing, and enterprise integrations.
- Build robust CI/CD pipelines and automate deployments to AWS/GCP with zero downtime.
- Ensure observability and reliability: metrics, tracing, logging, and proactive alerting.
- Work closely with ML and frontend teams to expose backend capabilities cleanly and efficiently.
- Provide technical mentorship to Product Engineer I/II teammates and influence best practices.
- Work closely with ML and frontend teams to expose backend capabilities cleanly and efficiently.
- Ensure observability and reliability: metrics, tracing, logging, and proactive alerting.
- Build robust CI/CD pipelines and automate deployments to AWS/GCP with zero downtime.
- Drive API architecture for high-scale use cases — multi-tenant workloads, real-time processing, and enterprise integrations.
- Introduce caching systems (Redis, Memcached, or similar) to deliver low-latency, high-throughput APIs.
- Implement queuing and streaming systems (Kafka, NATS, RabbitMQ, or equivalents) to enable reliable event-driven workflows.
- Model and optimize data in PostgreSQL
What we are looking for:
- 4–6 years of backend engineering experience, with a proven track record of designing APIs for scale.
- Deep expertise in Node.js + TypeScript; strong grasp of async patterns and modern frameworks.
- Solid experience with PostgreSQL (schema design, performance tuning, transactions) and at least one caching system (Redis/Memcached).
- Familiarity with queuing/streaming platforms (Kafka/NATS/RabbitMQ).
- Bonus: experience with Python, especially for ML-related integrations.
- Strong fundamentals in distributed systems, concurrency, and API performance tuning.
- Hands-on with Docker, Kubernetes, AWS/GCP, and CI/CD workflows.
- You don't just build APIs; you design systems that stay fast, reliable, and cost-efficient at scale.
- You don't just build APIs; you design systems that stay fast, reliable, and cost-efficient at scale.
- Hands-on with Docker, Kubernetes, AWS/GCP, and CI/CD workflows.
- Strong fundamentals in distributed systems, concurrency, and API performance tuning.
- Bonus: experience with Python, especially for ML-related integrations.
- Familiarity with queuing/streaming platforms (Kafka/NATS/RabbitMQ).
- Solid experience with PostgreSQL (schema design, performance tuning, transactions) and at least one caching system (Redis/Memcached).
- Deep expertise in Node.js + TypeScript; strong grasp of async patterns and modern frameworks.
What you will get:
- A chance to build from 0 to 1 the AI backbone of a product targeting a $100M+ ARR opportunity.
- End-to-end ownership and visibility, your code will run in production, not in notebooks.
- A front-row seat to shape how agentic AI gets applied to real-world, high-stakes problems.
- End-to-end ownership and visibility, your code will run in production, not in notebooks.
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