Sr AI Engineer | Remote - Europe | TS/Vue/NodeJS
About this role
The AI orchestration of your wildest imagination. n8n is the open workflow orchestration platform built for the new era of AI. We give technical teams the freedom of code with the speed of no-code, so they can automate faster, smarter, and without limits. Backed by a fiercely inventive community and 500+ builder-approved integrations, we’re changing the way people bring systems together and scale ideas for impact. Since our founding in 2019, we’ve grown into a diverse team of over 260 - working across Europe and the US, connected by a shared builder spirit and with our centre of gravity in Berlin. Along the way, we’ve: Cultivated a community of more than 650,000 active developers and builders Earned 190K+ GitHub stars, making us one of the world’s Top 40 most popular projects Backed by top
About data / ml roles
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
Typical skills: SQL, Python, pandas, basic stats; ML roles add scikit-learn / PyTorch / TensorFlow; engineering roles add Airflow / dbt / Spark
Salary insights (US, rough)
Typical range for data / ml roles in the US is $85,000–$240,000/year, varying widely with seniority, company stage, and city.
Estimates only. For company-specific numbers, check levels.fyi (tech), Glassdoor, or ask in the interview.
How to prep for the interview
Data interviews split: analysts get SQL + case-study questions, engineers get pipeline-design + coding, scientists get stats + ML modeling + sometimes a take-home dataset. Read the JD carefully and prepare to the actual flavor — don't waste time on PyTorch for an analyst role.
Common questions across all three: "How would you measure success for [product feature]?", "Walk me through a project where the data told you something surprising", and SQL window functions (LAG, ROW_NUMBER, PARTITION BY — practice these specifically). For ML roles, expect a question about overfitting, bias-variance tradeoff, and how you'd debug a model that performs well in training and badly in production.
Where this role typically leads
Career paths in data/ML have gotten messier as the discipline has matured. The clearest progression: Junior Analyst → Senior Analyst → Analytics Manager on the analyst side, Junior Data Engineer → Senior → Staff on the engineering side, and Junior DS → Senior DS → Principal DS / ML Lead on the modeling side. Salary peaks are highest for ML engineers + research scientists at big-tech firms.
Cross-discipline moves are common: analysts who learn engineering often outearn engineers who didn't learn business context. Stay close to the business — the most valuable data people are the ones executives bring into strategy discussions, not the ones building dashboards alone.
Red flags to watch for
- "Data scientist" role that's really a SQL analyst. Read the responsibilities carefully — if it's all dashboards and ad-hoc queries, it's an analyst role with a science title.
- No mention of stakeholder collaboration. Data work in isolation is usually data work that gets ignored. The role needs to be embedded with a business team.
- "AI / ML" listed everywhere but no specifics. If they can't name the actual problems they want to solve with ML, the company is buzzword-shopping.
- Asking for "10+ years of Python" or impossible combinations. Either the JD was written by HR without input from a data team, or the company has unrealistic expectations.
Frequently asked questions
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What does a data / ml role typically involve?
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
What's the typical salary range for data / ml roles in the US?
Roughly $85,000–$240,000 USD/year, depending on seniority, location, and company stage. This is a wide range on purpose — verify against levels.fyi or Glassdoor for the specific company.
