# Docusign — Machine Learning Engineer

| Field | Value |
|---|---|
| **Date found** | 2026-05-24 |
| **Company** | Docusign |
| **Role** | Machine Learning Engineer |
| **Location** | Dublin, Ireland (Hybrid) |
| **Salary** | Undisclosed |
| **Job URL** | https://www.linkedin.com/jobs/view/4377628100/ |
| **Status** | New |

---

## Company Research

| Field | Value |
|---|---|
| **Headquarters** | San Francisco, CA, USA |
| **Founded** | 2003 |
| **Employees** | 5,001–10,000 (8,440 total; 7,946 on LinkedIn) |
| **LinkedIn** | https://www.linkedin.com/company/docusign/ — 583k followers |
| **Website** | https://www.docusign.com |
| **Blog** | — |

- **Product:** World's leading e-signature and intelligent agreement management (IAM) platform for contract creation, signing, and lifecycle management.
- **Customers:** 1.5 million customers and 1 billion+ people across 180 countries; Fortune 500 enterprises globally.
- **Notable:** Nasdaq-listed (DOCU); 60–70% market share in e-signatures; expanding into AI-powered contract lifecycle management through Iris engine; 2 Fenergo alumni, 3 Bologna alumni.

---

## Job Summary

**What they do:** Docusign is the global leader in e-signature and intelligent agreement management, unlocking business-critical data trapped in contracts.

**The role:** IC Machine Learning Engineer on the ML team, building NLP/ML systems for automated contract and document understanding.

**Core work:**
- Research, prototype, and deploy NLP/ML/DL models for contractual data (NER, POS tagging, text classification, clustering, summarisation)
- Maintain and extend rule-based and supervised/unsupervised NLP methods for contract clause recognition
- Deploy models to production and collaborate with Product to translate requirements into ML success metrics

**Stack:** Python · PyTorch · TensorFlow · spaCy · HuggingFace · scikit-learn · LLMs (GPT/Gemini/LLaMA — preferred)

**Work style:** Dublin Hybrid — minimum 2 days/week in office.

---

## Score: 41%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 15% | Traditional NLP engineering for contract understanding (NER, text classification, ML models). LLM experience is "preferred" not core. No agentic orchestration whatsoever. |
| Tech fit (25%) | 50% | Python, PyTorch, ML frameworks match. No LangGraph/LangChain/CrewAI/MCP. LLM experience listed as preferred only. |
| Remote fit (25%) | 50% | Hybrid minimum 2 days/week — on the boundary of acceptability. |
| Company culture fit (15%) | 25% | Large 8,400-person public company in document/e-signature domain; slow-moving enterprise culture, not AI-native. |
| IC/leadership balance (10%) | 85% | Pure IC role reporting to ML Director. |
| **Final (weighted)** | **41%** | |

---

## Strengths

- Fenergo alumni and Bologna alumni at Docusign — warm network connections possible
- Purely IC ML engineering role with clear ownership
- Large stable company with good resources

---

## Weaknesses & Risks

- Near-zero agentic AI content — primarily traditional NLP for legal/contract documents
- Minimum 2 days/week hybrid in Dublin office — at or slightly above the location threshold
- Very large enterprise company (8,400+ people) — slow-moving culture, high bureaucracy
- No LangGraph/LangChain/agentic stack — would likely be a step backward from Luca's current trajectory
- Salary undisclosed from large public company

---

## Suggestions

- Low priority — apply only after exhausting higher-scoring options
- Could leverage Fenergo/Bologna network for internal referral if pursuing

---

## Interview Tracker

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