Forward Deployed AI Engineer
About this role
A World-Changing Company Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more. The Role Forward Deployed AI Engineers work directly with customers owning Gen AI strategy and implementation. On a daily basis, you will build end-to-end workflows, take them to production, and solve real world problems at the largest scale. You will have ample opportunity to contribute learnings from the field back to the Palantir AIP product suite. You will be on the forefront of extending Palantir's existing footprint and strategy into new markets and problem spaces opened up by Gen AI. Core
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.
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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.