Backend Engineer (Infrastructure & Platform) (f/m/d)

Das ist der Job

The opportunityLLMs are changing analytical work.

Darum lohnt es sich

Capabilities that once required large teams of highly paid data engineers are becoming accessible to smaller companies for the first time.

You're here to learn from customers and take bets on a product that doesn't exist yet, not to learn how to write software.Strong TypeScript and cloud infrastructure experienceEnglish C1 or above; German a plus What we offer • Munich is our hub. • In-person by design, we support your relocation within Europe. • Regular team offsites for product sprints and staying close to the frontier. • Zeit AI package: daily lunch allowance, covered dinner in the office, wellpass membership, best tech & tools€5k referral bonus if we hire someone you refer.

About the interview process • First call with ElisaTech screening • Tech interview with JonasDecomposition interview • Second technical interview • Behavioural interview • Reference check • Take-home case + Meet the team At Palantir, we delivered real data and BI value, but deployments never scaled without expensive, hands‑on engineers.

We believe LLMs change that, and we have the customers and revenue to prove the model works.Now we scale and one key lever is the platform. Every new customer brings new data sources, more rows to sync, and more queries to serve.

Your job is to make sure ZeitMind, our autonomous data engineer, can handle onboarding 10 new enterprise customers per week and the hard part isn't the compute.

It's capturing each business's context fast enough: connecting messy data systems, making sure the agent's answers are correct, that visualisations hold up, and that the customer is able to get value out of the product quickly. This is as much a product and correctness problem as an infrastructure one, and it hasn't been solved before.

Onboarding a customer should be boring. This is the role that lets everything else scale.

What you will do • Build the sync layer: millions of rows from ERP, CRM, and homegrown systems, ingested incrementally and reliably, without an engineer babysitting the pipeline • Cut onboarding time: connecting a new customer's data sources should take hours, not weeks.

You abstract sources so our agents work with any of them the same way • Make the agent fast where it counts: speed comes from tool design that parallelizes, sub-agents, and branching, not tokens per second.

You design tools so work can run concurrently and safely • Route data safely between customer networks and ours: security and reliability are features our customers pay for • Build the guardrails for correctness: automatic checks and integrated validation tooling so the agent's output can be trusted, and so it flags what a human should verify • Keep the platform simple: choose boring technology where boring wins, and be able to say why every system we run earns its place You will thrive here if youhave built or scaled data platforms before and think clearly about data processing architectureshave a deep understanding of OLAP and OLTP systems and when to reach for eachbring strong backend experience with TypeScript and are at home in cloud infrastructureget foundations right without overengineering them; you build for the scale we will hit next year, not for a hypothetical onetake full ownership from idea to production to impact, and are comfortable working without predefined specswant to build foundations early rather than optimize mature systemsare genuinely interested in the data and agentic space and how LLMs enable new workflows for non-technical users Requirements You've been a lead architect or equivalent: built many systems yourself, and seen how large systems fail and evolve.

Job/Process management, Docker, VPN/routing and Agentic frameworks: harness, tool calls, sandboxed tools, branching of data and specs.