# Nebius — Staff / Principal Applied AI Researcher (Agentic Search)

| Field | Value |
|---|---|
| **Date found** | 2026-05-20 |
| **Company** | Nebius |
| **Role** | Staff / Principal Applied AI Researcher (Agentic Search) |
| **Location** | EMEA Remote |
| **Salary** | Undisclosed |
| **Job URL** | https://www.linkedin.com/jobs/view/4400224489/ |
| **Status** | New |

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## Company Research

| Field | Value |
|---|---|
| **Headquarters** | Amsterdam, Netherlands |
| **Founded** | 2024 (spun out of Yandex N.V., rebranded to Nebius Group N.V. in August 2024; Yandex founded 1997) |
| **Employees** | ~1,500 (1,190 on LinkedIn) |
| **LinkedIn** | [company/nebius](https://www.linkedin.com/company/nebius/) — 93K followers |
| **Website** | [nebius.com](https://nebius.com) |
| **Blog** | [nebius.com/blog](https://nebius.com/blog) |

- **Product:** GPU cloud infrastructure, managed model serving, fine-tuning, and developer tooling for the global AI economy (Nasdaq: NBIS)
- **Customers:** AI labs, developers, and enterprises building on GPU compute — major contracts with Microsoft ($17B) and Meta ($3B)
- **Notable:** Spun out of Yandex's international assets in 2024 following the Russia-Ukraine conflict; rebranded as Nebius Group N.V.

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## Job Summary

**What they do:** AI-native cloud company (Nasdaq: NBIS) providing GPU cloud infrastructure, managed model serving, fine-tuning, and developer tooling for the global AI economy.

**The role:** Staff / Principal IC in the Agentic Search team — end-to-end ownership of an agent-native web retrieval platform.

**Core work:**
- Design multi-step retrieval architectures for AI agents to query, plan, and reason over live web data
- Ground LLMs in real-time web data at scale — retrieval, reranking, result synthesis
- Own applied research direction from experimentation through to high-throughput production
- Mentor engineers and set technical direction for the team

**Stack:** Python · Go/C++ (preferred) · LLMs · RAG · embeddings · hybrid search · transformer architectures

**Work style:** Fully remote, EMEA

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## Score: 82%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 75% | Core focus is agentic retrieval — systems where AI agents plan, query, and reason. Applied and production-facing, not just research. Slightly tangential to multi-agent orchestration but clearly agentic. |
| Tech fit (25%) | 70% | Strong overlap: Python, LLMs, RAG, embeddings, hybrid search, transformer architectures. Minor gap: Go/C++ listed as preferred alongside Python. Luca's retrieval and RAG experience maps well. |
| Remote fit (25%) | 100% | Fully remote, EMEA. No on-site requirements mentioned. |
| Company culture fit (15%) | 80% | AI-native, serious technical culture, fast-moving, high autonomy expected. Larger than a startup (1,400+) but retains engineering-led culture signals. |
| IC/leadership balance (10%) | 85% | Staff/Principal IC with mentoring, end-to-end ownership. No management implied. |
| **Final (weighted)** | **82%** | |

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## Strengths

- Fully remote with no on-site obligation — perfect location fit
- Staff/Principal level: appropriate seniority, ownership, and impact scope
- "Agent-native" framing aligns directly with Luca's agentic AI focus
- RAG and retrieval experience from medical knowledge base and compliance work maps directly
- Bologna alumni connection noted — warm intro path exists
- Applied research with production shipping expectation (not pure research)

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## Weaknesses & Risks

- Title says "Researcher" which may imply more academic cadence than Luca prefers
- Salary undisclosed — needs to be confirmed ≥ €110k early in process
- Go/C++ preferred alongside Python — minor gap but worth monitoring
- 100+ applicants clicked apply — competitive pool
- Retrieval/search as the primary domain is adjacent but not identical to Luca's multi-agent system design focus

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## Suggestions

- Lead with the medical RAG system (5,000+ papers, 43% retrieval improvement) and compliance document pipeline — directly maps to "grounding LLMs in real-world data at scale"
- Emphasise the applied research → production track record: six production AI systems at the cybersecurity company, 90%+ extraction accuracy at Fenergo
- Reach out via the Bologna alumni on LinkedIn before applying cold — there are school alumni at Nebius per the posting
- Ask early about salary range to avoid wasting both sides' time

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## Interview Tracker

| Stage | Date | Notes |
|---|---|---|
| Applied | | |
| Recruiter screen | | |
| Technical interview | | |
| Final round | | |
| Offer / Outcome | | |
| Rejected | 2026-05-25 | specs too high for my level |