# Jobgether (client undisclosed) — Senior Machine Learning Scientist

| Field | Value |
|---|---|
| **Date found** | 2026-06-01 |
| **Company** | Jobgether (client: undisclosed AI-native marketplace/SaaS startup) |
| **Role** | Senior Machine Learning Scientist |
| **Location** | Ireland (Fully Remote, EU timezones) |
| **Salary** | Undisclosed + early-stage equity |
| **Job URL** | https://www.linkedin.com/jobs/view/4422871891/ |
| **Status** | New |

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## Company Research

Recruiting platform — client undisclosed. Jobgether posts on behalf of an undisclosed partner company. Based on JD content (recommendation systems, marketplace/booking metrics like "bookings and revenue," "consumer behavior problems at scale," early-stage equity), the client appears to be an AI-native marketplace or consumer tech startup.

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## Job Summary

**What they do:** AI-native marketplace or consumer tech startup (client undisclosed) using ML for recommendations, personalisation, and agentic experiences at scale.

**The role:** Senior ML Scientist owning end-to-end ML system development from problem framing to production deployment, with high autonomy and direct product strategy influence.

**Core work:**
- Design and ship recommendation systems, ranking models, predictive analytics, and LLM-powered user experiences
- Build agent-based architectures and RAG pipelines embedded in product features
- Define evaluation frameworks, experimentation processes, and ML best practices across the org

**Stack:** Python · SQL · RAG · Vector databases · LLM APIs · Embedding models · Prompt engineering · LLM evaluation frameworks · LLMOps (Langfuse/Datadog) · LangChain/LlamaIndex/LangGraph (preferred)

**Work style:** Fully remote, Europe/UK timezones, async-first culture, high autonomy.

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## Score: 68%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 45% | Agent-based architectures mentioned as a key area; LLM-powered applications are a core focus but primary emphasis is on recommendation systems and ML science rather than multi-agent orchestration |
| Tech fit (25%) | 60% | Python, RAG, vector databases, LLM APIs, evaluation frameworks — solid overlap; LangGraph mentioned as preferred but not required |
| Remote fit (25%) | 100% | Fully remote, EU/UK timezones, flexible async |
| Company culture fit (15%) | 55% | Described as "AI-native engineering culture," rapidly scaling startup with high ownership — fits profile well; client identity unknown is the main risk |
| IC/leadership balance (10%) | 80% | Scientist leading initiatives end-to-end, mentoring role, no management duties |
| **Final (weighted)** | **68%** | |

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## Strengths

- Fully remote (EU timezones), async-first culture — ideal work style match
- Early-stage equity participation — upside potential
- High autonomy, IC ownership from problem framing to production
- Strong AI-native culture signal: "rapidly scaling technology company"
- LLM/RAG/agent work embedded in product features

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## Weaknesses & Risks

- ⚠️ Salary undisclosed — equity compensation at early-stage startup carries risk
- Client company identity unknown — culture, stage, and domain unverified
- Primary orientation is ML Science (recommendations, ranking) rather than deep agentic AI engineering
- PhD/research experience "highly valued" — publication track record not Luca's profile

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## Suggestions

- Ask Jobgether to disclose the client before investing heavily in the process
- Emphasise production ML system ownership (Fenergo agentic pipeline, RAG systems)
- Highlight recommendation/ranking adjacent work from text classification and semantic search
- Clarify equity stage (pre-seed vs Series A/B) before accepting

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## Interview Tracker

| Stage | Date | Notes |
|---|---|---|
| Expired | 2026-06-09 | Posting expired — no longer accepting applications |
| Applied | | |
| Recruiter screen | | |
| Technical interview | | |
| Final round | | |
| Offer / Outcome | | |