We’re Cortea, a Berlin startup transforming audits with AI. Manual, document-heavy audits waste expert time while demand keeps rising. Our AI-powered software and specialized AI agents remove the repetitive work so auditors can focus on judgment.
Backed by top-tier VCs with >15m EUR funding, with a working product and paying customers, we’re rapidly scaling.
We value first-principles thinking, speed, trust, and kindness. We build side by side in our Berlin office.
Your RoleWe are looking for an engineer with strong backend, data, and AI systems experience to build the evaluation and observability foundation for production-grade LLM agents used in complex audit workflows.
This role sits at the intersection of backend engineering, data infrastructure, and AI quality. You will build the evaluation systems that power our multimodal retrieval agents and continuously improve critical quality metrics across current and future pipelines.
You’ll work at the edge of applied AI and information retrieval, building multimodal agentic pipelines and solving hard context and agent-harness engineering problems.
This is not a traditional analytics, BI, or dashboarding role. You should expect to write production code, design data architecture, work inside backend systems, and directly improve the quality, cost, reliability, and performance of LLM-based agents.
What you’ll doYou will help build and operate the technical systems around our AI agents, with a focus on data infrastructure, evaluation, observability, and optimization. You will:
Build online and offline evaluation systems for LLM agents, including pipelines that use golden datasets, ground-truth data, human review workflows, and experiment results.
Create automated quality gates so changes to prompts, context, models, or agent logic can be tested before reaching production.
Analyze large volumes of agent traces and executions in columnar and analytical databases such as BigQuery or ClickHouse to identify failure modes, quality regressions, latency issues, reliability gaps, and cost optimization opportunities.
Build reliable data retention and replay mechanisms for long-term analysis of production agent behavior.
Manage observability tools for tracing, monitoring, debugging, and experiment management of our audit agents.
Team up with backend engineers to improve the speed and reliability of our retrieval and reasoning agents.
Have strong Python and/or backend engineering experience.
Have a solid understanding of how LLM and agent systems are evaluated—including deterministic checks, ground truth, LLM-as-judge, human review, and quality metrics—and can reason about when each approach is appropriate.
Have deployed and operated systems in the cloud, ideally on GCP.
Have hands-on experience building end-to-end retrieval or ML pipeline evaluation systems and using LLM observability or experimentation tools such as Braintrust, MLflow, Langfuse, or Weights & Biases.
Are comfortable working with analytical databases, data warehouses, columnar stores, and high-volume event or trace data.
Understand system design, reliability, observability, monitoring, logging, debugging, and operational trade-offs.
Bring senior-level engineering judgment: you can make architectural decisions, communicate trade-offs, and build systems that other engineers can extend.
Are comfortable with ambiguity, able to reason from first principles, and excited to build infrastructure for AI systems that are actively used in production.
Designing data pipelines, ETL/ELT workflows, event-processing systems, or feedback loops for production data.
Building infrastructure around LLM-based products or agentic systems, including optimizing LLM usage, context windows, reasoning tokens, or model selection.
Working with production traces from complex distributed systems.
Building internal platforms for engineers, domain experts, or operations teams.
Using workflow orchestration systems such as Temporal or similar.
Familiarity with audit, finance, compliance, or other high-accuracy domains.
Experience in an early-stage startup or fast-moving engineering environment.
No-one checks every box. If you’ve shipped retrieval systems and like owning evaluations and pipelines, let’s talk.
What we offerHigh impact & growth: Shape strategy at a scaling AI startup from day one.
Mission-driven culture: Ambitious team valuing first-principles thinking and bold ideas.
Attractive compensation: competitive salary plus significant equity.
Best tools for the job: Generous coding tools budget so you can use the best tools for the job.
Startup perks: Flexible vacation, team lunches, retreats, central Berlin office.
First Call — Intro to Cortea with Liza
Second Call — Technical interview with Vlad
Third Call — Deep dive into our culture with our Co-Founder Philipp
On-site Day (Berlin) — Meet the team and work on a real problem together
We’re an equal-opportunity team and encourage women and underrepresented groups to apply.
Skills Required
- Strong Python and/or backend engineering experience
- Understanding of LLM and agent evaluation methods, including deterministic checks, ground truth, LLM-as-judge, human review, and quality metrics
- Experience deploying and operating systems in the cloud, ideally on GCP
- Hands-on experience building end-to-end retrieval or machine learning pipeline evaluation systems
- Experience with LLM observability or experimentation tools such as Braintrust, MLflow, Langfuse, or Weights & Biases
- Comfort working with analytical databases, data warehouses, columnar stores, and high-volume event or trace data
- Understanding of system design, reliability, observability, monitoring, logging, debugging, and operational trade-offs
- Senior-level engineering judgment and ability to make architectural decisions and communicate trade-offs
- Comfort with ambiguity and first-principles reasoning
- Experience designing data pipelines, ETL/ELT workflows, event-processing systems, or production feedback loops
- Experience building infrastructure around LLM-based products or agentic systems
- Experience optimizing LLM usage, context windows, reasoning tokens, or model selection
- Experience working with production traces from complex distributed systems
- Experience building internal platforms for engineers, domain experts, or operations teams
- Experience using workflow orchestration systems such as Temporal
- Familiarity with audit, finance, compliance, or other high-accuracy domains
- Experience in an early-stage startup or fast-moving engineering environment
What We Do
We are building the best-in-class solution for audit automation. Experience AI with maximum impact, ease, and tracability.
