Senior Software Engineer

About Enterpret

At Enterpret, we are building the customer feedback intelligence platform that turns unstructured customer feedback into a structured, queryable source of truth — solving complex problems in natural language processing, serverless computing, and analytics on the frontend, pushing the envelope of what’s possible by applying first principle thinking.

Kleiner Perkins and Sequoia Capital back us because they share our conviction that product development teams deserve a better way to understand their customers. That conviction is validated by the respected product teams who rely on us every day, including Canva, Notion, Samsung, and Loom. We love working with folks who are resourceful, thrive in ambiguity, and display a strong sense of ownership.

Our engineering culture is built around leveraging and contributing to open-source tools. We aim to build upon and improve state-of-the-art systems in our field of work.

Read more about our team, core values, and operating principles - here .

What You’ll Do

• Design and deliver significant Graph Platform components end to end — high- and low-level design, implementation, rollout, and the follow-through after launch.
• Improve reliability, latency, performance, and operational readiness across the knowledge graph, data systems such as DynamoDB and ClickHouse, Model Repository, LLM self-hosting platform, and the high-throughput pipelines that run our models.
• Take production issues beyond the immediate symptom — turn recurring failures into durable fixes , not patches.
• Build with metrics, alerts, runbooks, tests, and rollback plans as a core part of the work, so every change is safe and observable in production.
• Own on-call and production responsibility for your area, and establish the operational baseline: failure modes, alerts, runbooks, and the gaps that need closing.
• Partner with product engineers, product managers, SRE, and adjacent teams where your component has dependencies — and work closely with the people shaping the longer-term platform direction.
• Continuously raise the engineering-quality bar through code review, testing, documentation, and support for more junior engineers.

What It Takes

• 4+ years in software engineering, with evidence you can own a meaningful production component from conception to deployment.
• 2+ years at one organization where you’ve designed, built, deployed, and operated a significant backend, data, or infrastructure component — not just shipped code, but kept it running well.
• Comfort working through moderate ambiguity you clarify unclear requirements, decide what needs to be built now, make design trade-offs across components, and deliver without constant direction.
• Strong engineering-quality and operational habits: comprehensive tests, edge-case thinking, metrics, alerts, runbooks, rollback plans, and real production debugging experience.
• Hands-on experience with high-throughput data systems or asynchronous pipelines — exposure to at least one of DynamoDB, ClickHouse, Snowflake, or model hosting/inferencing is valuable, and prior experience with Golang, Python, or another object-oriented programming language is preferred, though direct experience with our exact stack is not required.
• An AI-native working style you use AI tools to move much faster while validating outputs, preserving quality, and sharing useful workflows with the team.
• A track record of mentoring and collaborating — you raise the standard of the engineers and systems around you.

What Success Looks Like

First 90 days

• Builds working context on the Graph Platform stack and takes on-call and production ownership seriously.
• Owns a scoped production problem or component improvement from design through deployment.
• Establishes the operational baseline for their area: relevant metrics, failure modes, alerts, runbooks, and gaps.
• Produces designs that make the trade-offs explicit and can be implemented without significant rework.

By 6 months

• Independently delivers a significant team-level component or feature from conception to deployment.
• Has made a visible improvement to a Graph Platform outcome: reliability, latency, throughput, correctness, cost, or operational load.
• Is a reliable owner in production: issues are investigated systematically, and follow-up fixes are durable rather than patches.
• …