Junior Machine Learning Engineer

Building the bank of tomorrow takes more than skills. 
It means combining our differences to imagine, discuss, code, develop, test, learn… and celebrate every step together. Share our vibes? Join Swissquote to unleash your potential.

We are the Swiss Leader in Online Banking and we provide trading, investing and banking services to +650’000 clients through our performant and secured digital platforms.

Our +1200 employees work in a flexible way, without dress code and in multicultural teams. 
By having a huge impact on the industry, they are growing their skills portfolio and boosting their career in a fast-pace environment. Have a look behind the scenes by checking Humans of Swissquote on instagram.

We are all in at Swissquote. As an equal opportunity employer, we welcome candidates from all backgrounds, experiences and perspectives to join our team and contribute to our shared success.

Are you all in? Don’t be shy, apply!

Our AI systems are already delivering measurable impact across the bank, from classical ML models powering core business processes to a growing suite of LLM-powered applications transforming how teams work. As we scale this portfolio, we need engineers who can push the boundaries of what's possible while keeping production systems robust and performant.

The Data Science team is looking for a Junior Machine Learning Engineer to design, build, and ship AI-powered solutions — with a strong emphasis on Large Language Model applications. You will be involved in projects end-to-end: from prototyping and experimentation through to production-grade systems serving real users. This is a high-impact role where you will take on real responsibility early and shape how AI is built and delivered across the organization.

As a Junior Machine Learning Engineer You Will:

• Build LLM-Powered Applications: Design and develop applications leveraging Large Language Models — including RAG systems, agentic workflows, and conversational interfaces — tailored to complex business needs in a regulated environment.
• Develop & Fine-Tune Models: Train, fine-tune, and evaluate both classical ML and language models, selecting the right approach for each problem and optimizing for production constraints such as latency, cost, and accuracy.
• Engineer for Production: Build reliable, scalable ML services and APIs. You care about code quality, testing, and maintainability as much as model performance.
• Evaluate & Iterate: Contribute to evaluation frameworks for AI applications — particularly for generative systems where traditional metrics fall short — and use them to drive continuous improvement.
• Stay on the Cutting Edge: Actively track the fast-moving LLM landscape, assess emerging tools and techniques (new model releases, orchestration frameworks, prompting strategies), and translate them into practical value for the team.
• Collaborate Across Teams: Work closely with data scientists and MLOps engineers, product owners, and business stakeholders to translate business problems into well-scoped AI solutions.

• Educational Background: Degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related field.
• Python Proficiency: Strong programming skills in Python. You write clean, maintainable code and care about engineering best practices.
• LLM & NLP Experience: Hands-on experience building applications with Large Language Models (prompt engineering, RAG, fine-tuning, agent frameworks) — whether through professional experience, personal projects, or academic work.
• ML Fundamentals: Solid grounding in classical machine learning and deep learning. You can pick the right tool for the job, whether that's a gradient-boosted tree or a transformer.
• Full-Stack Comfort: You are able to build beyond the model — whether that means spinning up an API, putting together a web interface, or wiring up a data pipeline.
• Production Mindset: Familiarity with deploying models or applications into production. Experience with containerization (Docker, Kubernetes) and CI/CD is a strong plus.
• Evaluation & Critical Thinking: You understand the challenges of evaluating generative AI systems and can think critically about designing meaningful benchmarks beyond simple accuracy metrics.
• Communication: Fluent in English, able to collaborate effectively with both technical peers and non-technical stakeholders.
• Nice to Have: Experience with MLflow, fine-tuning open-source LLMs, or frontend frameworks (React, Streamlit, Gradio).