Member of Technical Staff, Platform

• 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.

• 6+ years in software engineering, with evidence you can own a meaningful production component from conception to deployment.
• 3+ 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.

• 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.

• 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.
• Raises the engineering-quality bar through code review, testing, documentation, and support for more junior engineers.

• Is the go-to owner for a meaningful Graph Platform area.
• Has delivered and operated multiple improvements that make the team's systems more robust, scalable, and maintainable.
• Can take partially ambiguous requirements, clarify what matters, make team-level trade-offs, and lead delivery with limited oversight.
• Has created clear runway toward L5 through broader component ownership and influence on adjacent work.

Why Enterpret?

• High Impact: Own systems that are already in production and need to scale well — the core data systems, models, and pipelines where Enterpret's platform comes together.
• Ownership: End-to-end responsibility for features and systems.
• Complex Challenges: Work on the knowledge graph, distributed data tiers, model hosting, and high-throughput enrichment pipelines where a single change can touch storage, query paths, reliability, and cost.
• Growth: Learn and grow with a high-caliber team, with a clear path from owning components to shaping larger slices of Graph Platform and into L5.
• Culture: Open, collaborative, and values-driven environment with autonomy.
• Benefits: Competitive salary, equity, hybrid work setup, premium healthcare, and more.

At Enterpret, we operate with a deep sense of ownership — we play for the team and do what it takes to win together. We care personally for our teammates while pushing each other with honest, actionable feedback. Above all, we approach everything with humility and a drive to keep learning and getting better.

Equal Opportunities

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.