Machine Learning Engineer for Financial Services

About Us

We are a Machine Learning and Computer Vision startup founded in 2020, headquartered in Dublin, Ireland, with an AI Lab in Milan, Italy.

Our expertise spans Machine Learning and Generative AI for financial services and Computer Vision for life sciences.

At Gemmo AI, we build custom AI solutions that combine automation with human insight. We use a modular approach: first we explore the highest-impact opportunities, then we design and deploy tailored solutions, and finally we help improve and maintain them over time.

We believe in responsible, pragmatic AI: systems that integrate into real workflows, provide measurable value, and remain under your control.

About the Role

We are looking for a Machine Learning Engineer with 1-2 years of experience to join our team and help develop, deploy, and integrate ML models into our clients’ cloud infrastructures.

In this role, you will design, build, and experiment across diverse areas of financial services, working with both global market data and proprietary product datasets.

You will collaborate closely with UI/UX designers, as well as frontend and backend developers, ensuring that AI components integrate into end-to-end solutions.

Beyond implementation, you will play an active role in shaping and refining our pipelines, maintaining scalability, reliability, and performance as we continue to innovate.

Compensation

• Full-time contract (tempo indeterminato);
• Up to 38k€ RAL, depending on experience;
• Yearly bonus based on KPIs fulfilment.

Recruiting Process

1. HR Screening (15 min): Company and role presentation, alignment on expectations.
2. Technical Interview (60 min): Technical discussion on ML principles and system design. No whiteboard coding or leetcode-style questions.
3. Final interview with CEO (15 min): Final Q&A round, alignment on project.

Working at Gemmo

Tech Stack

We work with a fairly broad spectrum of languages and tools, albeit with uneven distributions. Expect to get acquainted with the following:

• Languages: Python, Typescript, SQL, Rust
• ML & Data Libraries: PyTorch, Polars, Ultralytics, vLLM, Prophet, XGBoost, NumPy, OpenCV, Kornea, LangGraph
• IDE and Terminal: Cursor, Warp
• API Frameworks: FastAPI, Express.js, Tokio
• Tooling: uv, ruff, ty, Justfile
• Monitoring: CometML, Logfire, Prometheus, Grafana, Sentry, Langfuse, MixPanel
• Databases: PostgreSQL, Snowflake, DynamoDB
• CI/CD: Docker, GitHub Actions
• Infrastructure as Code: AWS CDK, Terraform, Ray, Modal
• Cloud Platforms: AWS, Azure

Working Hours

Monday-Thursday: 8.30 - 17:45 (CET)

Friday: 8:30 - 16:30 (CET)

Lunch time: 13:00 - 14:00 (CET, flexible)

On-call duty: not requested

Internal Meetings

We strive to minimize the number of fixed internal meetings. We prefer to have on-demand meetings when necessary and involving only the relevant people.

We do have a few meetings that are fixed, though:

1. Morning standup (8:30, 10 minutes): definition and review of daily tasks;
2. Evening standup (17:45 Mon-Thur / 16:30 Fri, 10 minutes): wrap up and updates;
3. Weekly retrospective (Fridays, 8:30, 1 hour): projects status review;
4. Monthly 1:1s with CTO (first Friday of the month, 20 minutes): discussion on problems, roadblocks, expectations, overall satisfaction.

Hybrid Work Policy

Required 3 days per week in person (Via Zuretti 34, 20125, Milan) if within 1 hour of commute time. Else, 2 days per week if within the Milan metropolitan area.

Mandatory

• Master’s degree in Data Science, Machine Learning, Computer Science, Physics, Math, Engineering or similar;
• Proficiency in Python and SQL;
• Familiarity with libraries such as Polars, XGBoost and PyTorch;
• Knowledge of version control systems (e.g., Git);
• B2+ English proficiency;

Nice to Have

• Experience with databases and warehouses (Postgres, Snowflake);
• Experience with interaction with LLMs (GPT, Claude, Gemini) via API calls;
• Experience with cloud providers (AWS or Azure);

• Laptop, monitor, camera and noise-cancelling headphones;
• Paid lunch and coffee breaks when in office;
• Up to 20 days per year of work-from-anywhere.