Staff Engineer
Staff Engineer
TypeScript, Node.js, PostgreSQL, LLM Systems
Why join Levellr
Levellr is the enterprise community intelligence and management solution for Discord and Reddit, used by some of the world’s largest gaming companies, including Scopely, Krafton and Epic, as well as brands such as Google, YouTube and SoundCloud, to help them grow, manage and monetise their communities.
Discord and Reddit communities can generate millions of messages, but for the teams running them, it is hard to turn all of that activity into useful insight. Levellr helps them see what matters, understand their members, spot trends, improve engagement and make better commercial decisions from their community data.
The product is now moving into a more technically demanding phase. We are building AI-powered systems into the core of Levellr , including agents, evaluation pipelines, anomaly detection, cost infrastructure and LLM-powered workflows. These systems need to make sense of large, messy, fast-moving community data, and they need to work properly in production.
What you’ll work on
A big part of the role is leading the architecture and delivery of foundational systems across Levellr ’s AI and data platform.
That includes production agent systems, evaluation pipelines, anomaly detection, cost infrastructure, data models, orchestration patterns and internal frameworks that help the rest of the team build faster and with more confidence.
The data side really matters - Levellr processes millions of Discord and Reddit messages, so we need someone who understands what it takes to design, tune and evolve relational systems at scale. PostgreSQL is a big part of that. Indexing, partitioning, query performance, schema design, migrations and data modelling are central to the role, not just useful extras.
The AI side needs to be practical too - We need someone who has seen what happens when LLM systems meet real users, real data, real cost and real failure modes. You will help shape how Levellr thinks about agents, model behaviour, evaluation, quality, observability, cost control and recovery patterns.
You will work closely with product, design, customer success and leadership. Some problems will be clearly scoped. Many will not be. A lot of the value in this role comes from taking a vague problem space, working out what matters, and turning it into something useful that ships.
Wider team impact - The right person will become a technical reference point for other engineers. Not by creating lots of processes or sitting above the work, but by building patterns, writing clear PRs, sharing good Looms, making sensible architectural calls, and helping the team move faster without getting loose.
How we ship
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Levellr has a strong bias towards shipping and learning from production.
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We do not wait until everything feels perfect. We ship the next sensible version, get it into real usage, then improve based on what we see.
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Some of our most important systems, including Levellr AI, agent quality evaluation and anomaly detection, have gone from blank page to shipped foundations in weeks, not quarters.
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That pace only works if the engineering judgement is strong.
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Good engineering here means knowing when to keep something simple, when to invest properly in foundations, when to refactor, when to ship and come back, and when to admit the first approach was wrong.
The level we are looking for
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Strong candidates will have deep experience with relational data at real scale. That means production systems where volume, query performance, indexing, partitioning, schema design or database architecture genuinely mattered.
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You should have built or led technical work that other engineers, teams or products depended on. This could be platform work, data infrastructure, pipeline orchestration, evaluation systems, cost infrastructure, architecture refactors or similar foundational work where the impact compounds over time.
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Production AI or