# StackAdapt — Applied Machine Learning Scientist

| Field | Value |
|---|---|
| **Date found** | 2026-06-07 |
| **Company** | StackAdapt |
| **Role** | Applied Machine Learning Scientist |
| **Location** | Ireland / UK / Germany — fully remote |
| **Salary** | Undisclosed (competitive stated) |
| **Job URL** | [linkedin.com/jobs/view/4414207454](https://www.linkedin.com/jobs/view/4414207454/) |
| **Status** | New |

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## Company Research

| Field | Value |
|---|---|
| **Headquarters** | Toronto, Canada (210 King St E, Suite 500) |
| **Founded** | 2013 |
| **Employees** | ~1,750 (1,747 on LinkedIn) |
| **LinkedIn** | [company/stackadapt](https://www.linkedin.com/company/stackadapt/) — 93k followers |
| **Website** | [stackadapt.com](https://www.stackadapt.com) |
| **Blog** | [stackadapt.com/careers/data-science](https://www.stackadapt.com/careers/data-science) |

- **Product:** AI-powered programmatic advertising DSP — 465 billion automated optimisations per second; connects brand and performance marketing across digital channels.
- **Customers:** Digital marketers and ad buyers across North America and EMEA; 1,000+ enterprise clients.
- **Notable:** #1 DSP on G2 for 6 consecutive years; $100M–$500M revenue (Indeed); 50% company-wide 2-year growth; G2 Top Software 2026.

Indeed rating: 3.6/5 (culture 4.0, WLB 3.7, comp 4.3)

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## Job Summary

**What they do:** StackAdapt builds an AI-powered programmatic advertising platform processing millions of requests/second with ML-driven bidding and campaign optimisation.

**The role:** Applied Machine Learning Scientist — IC contributor innovating ML algorithms to maximise advertising ROI and performance at scale.

**Core work:**
- Innovate ML algorithms for advertising performance — new approaches and improvements on state-of-the-art methods
- Write production code (with Data Engineers) to implement novel ML algorithms
- Prototype algorithms, test on historical data, iterate based on insights

**Stack:** Python · ML algorithms · statistics · optimisation · data structures · PyTorch/TF/JAX (implied)

**Work style:** Fully remote, Ireland/UK/Germany.

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## Score: 52%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 10% | Zero agentic AI content — role is traditional ML for advertising optimisation (bidding algorithms, campaign performance). No LLM, RAG, or orchestration. |
| Tech fit (25%) | 40% | Python/ML frameworks overlap; role focuses on statistics, optimisation, and ML algorithms for ads — not Luca's LangChain/LangGraph/Claude stack. |
| Remote fit (25%) | 100% | Fully remote Ireland/UK/Germany — no caveats. |
| Company culture fit (15%) | 45% | Not AI-native (ads tech platform); well-funded and fast-growing but commercial advertising culture, not AI-first product engineering. |
| IC/leadership balance (10%) | 80% | Pure IC role, collaborative with engineers. |
| **Final (weighted)** | **52%** | Salary undisclosed — no deduction, flag as risk. Low score driven by near-zero agentic AI depth. |

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## Strengths

- Fully remote Ireland — clean location fit
- At-scale ML problems (millions of requests/second, billions of decisions) — technically interesting
- Python-primary production ML — overlaps with Luca's ML background
- Company growing fast (50% 2-year growth); #1 DSP on G2

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## Weaknesses & Risks

- ⚠️ Zero agentic AI content — this is classical ML optimisation for advertising, directly opposite to Luca's target focus area
- Masters/PhD preferred — research-track bias
- Salary undisclosed — advertising tech may not match €110k+ floor
- Advertising domain is a weaker fit for Luca's compliance/enterprise AI background
- 294+ applicants — high competition

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## Suggestions

- Only consider if agentic AI pipeline dries up significantly
- If pursuing, emphasise production-grade Python ML systems (Fenergo, NDA cybersecurity), at-scale model deployment
- Ask: is there any LLM or agentic AI work planned in the team?
- Confirm salary range early — advertising DSP may cap below target

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## Interview Tracker

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