# Nebius — Senior Applied ML Engineer (Agentic Search)

| Field | Value |
|---|---|
| **Date found** | 2026-05-24 |
| **Company** | Nebius |
| **Role** | Senior Applied ML Engineer (Agentic Search) |
| **Location** | EMEA Remote |
| **Salary** | Undisclosed |
| **Job URL** | https://www.linkedin.com/jobs/view/4392196836/ |
| **Status** | New |

---

## Company Research

| Field | Value |
|---|---|
| **Headquarters** | Amsterdam, Netherlands |
| **Founded** | 2024 (spin-out from Yandex) |
| **Employees** | 1,001–5,000 (1,395 total; 1,206 on LinkedIn) |
| **LinkedIn** | https://www.linkedin.com/company/nebius/ — 93k followers |
| **Website** | https://nebius.com |
| **Blog** | — |

- **Product:** AI cloud infrastructure platform providing GPU compute, storage, and managed AI/ML services for AI-native companies and researchers.
- **Customers:** AI startups, enterprises, and research institutes globally needing scalable GPU infrastructure for training and inference.
- **Notable:** Nasdaq-listed; $2B investment from NVIDIA; 1,400+ employees including 400+ engineers; spun off from Yandex in 2024; rapidly expanding with R&D hubs across Europe, North America, and Israel.

---

## Job Summary

**What they do:** Nebius builds AI-native cloud infrastructure — GPU clusters, storage, and managed ML services — for the emerging AI economy.

**The role:** Senior Applied ML Engineer on the Agentic Search team, owning production ML systems for retrieval and ranking that power an agent-native web search layer.

**Core work:**
- Build and optimise embedding-based, hybrid retrieval and reranking systems at production scale (tens of thousands of workloads 24/7)
- Develop ML models for query understanding, document selection, and content ranking for AI agent consumption
- Define and improve evaluation frameworks and quality metrics; run experiments on latency, relevance, and cost trade-offs

**Stack:** Python · Go · C++ · Transformers · Embeddings · Vector search · Hybrid retrieval

**Work style:** Fully remote, EMEA. Fast-paced, product-driven environment with high ownership and autonomy.

---

## Score: 72%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 50% | The product is agentic search (agents doing retrieval/reasoning), but the engineer role is the ML layer (ranking, embeddings) rather than building agentic orchestration itself. |
| Tech fit (25%) | 55% | Python required; Go/C++ a plus. No LangGraph/LangChain in stack — core skills are IR/ranking and deep learning, not agentic frameworks. |
| Remote fit (25%) | 100% | Fully remote EMEA. |
| Company culture fit (15%) | 80% | Fast-moving AI-native startup/scale-up, well-funded, cutting-edge AI infrastructure. |
| IC/leadership balance (10%) | 85% | Senior IC engineer role with high ownership; no people management. |
| **Final (weighted)** | **72%** | |

---

## Strengths

- Fully remote EMEA with competitive compensation
- Nebius has Bologna alumni → potential warm introductions
- Agentic search is adjacent to Luca's agentic AI focus — real AI-native product context
- Company growing fast (137% 2-year headcount growth) — strong momentum

---

## Weaknesses & Risks

- Core work is ML/IR engineering (retrieval, ranking, embeddings) rather than building agentic workflows or LLM orchestration — potential career fit mismatch
- Stack is Python/Go/C++ for search systems, not LangGraph/LangChain/CrewAI — lower tech stack overlap
- Salary undisclosed — verify early
- Senior (not Staff/Principal) level — may not reflect current seniority trajectory

---

## Suggestions

- Emphasise experience with production ML systems, retrieval/ranking, and vector search if applicable
- Highlight the Bologna alumni network at Nebius (Job 001 already in index targets same company, Staff/Principal level)
- Ask about compensation band and whether Staff/Principal level is possible to negotiate into

---

## Interview Tracker

| Stage | Date | Notes |
|---|---|---|
| Applied | | |
| Recruiter screen | | |
| Technical interview | | |
| Final round | | |
| Offer / Outcome | | |