Job Description Template: Machine Learning Engineer
This template helps Swiss employers write a clear Machine Learning Engineer job posting. It covers core responsibilities (model development, data pipeline management, production deployment), must-have skills (Python, ML frameworks, statistics), nice-to-haves (cloud platforms, domain knowledge), and what to offer (salary range in CHF, Pensum percentage, benefits). Adapt canton/city, language requirements, and company-specific perks before posting.
A Machine Learning Engineer develops, trains, and deploys predictive models and ML systems that solve real business problems. In a Swiss SME context, this role often bridges data science and software engineering—you'll own the full lifecycle from prototype to production.
Use this template as a starting point. Replace [PLACEHOLDER] sections with your company's specifics: location (canton and city), Pensum (typically 80–100%), team structure, and technical stack. Mention whether German, French, Italian, or English is required for your canton.
About the Role
This Machine Learning Engineer position focuses on building and maintaining ML models that directly impact product or operations. You'll work with real datasets, iterate on model performance, and ensure systems scale reliably.
The role sits between data science and software engineering. You'll collaborate with product managers, data engineers, and backend developers to turn ML ideas into stable production systems.
- Design, train, and evaluate machine learning models using supervised and unsupervised techniques
- Build and maintain data pipelines that ingest, clean, and prepare data for model training
- Deploy models to production and monitor performance in real-world conditions
- Optimize model inference for speed and resource efficiency
- Conduct experiments and A/B tests to validate model improvements
- Document model logic, training procedures, and deployment processes for team knowledge
Must-Have Requirements
Candidates should demonstrate strong programming skills, ML fundamentals, and the ability to take models from notebook to production. Relevant work experience or substantial personal projects count.
Technical depth matters more than years in the field. We expect fluency in core ML concepts and honest awareness of knowledge gaps.
- Proficiency in Python and experience with scikit-learn, TensorFlow, or PyTorch
- Solid understanding of machine learning workflows: feature engineering, model selection, validation, overfitting
- Familiarity with version control (Git) and ability to write production-quality code
- Basic statistics: distributions, hypothesis testing, correlation vs. causation
- Problem-solving mindset and comfort debugging models empirically
- [LANGUAGE REQUIREMENT: specify German / French / Italian / English for your canton and team]
Nice-to-Have Skills & What We Offer
Experience with cloud platforms (AWS, Google Cloud, Azure), containerization (Docker), and MLOps tools accelerates onboarding but is not required. Domain knowledge in your industry is a bonus.
Competitive compensation reflects Swiss market norms. We offer a Pensum of [80–100%], a salary range of CHF [X] to CHF [Y] annually, and benefits that support wellbeing and growth.
- Experience with cloud ML services or MLOps tools (DVC, Weights & Biases, MLflow)
- Familiarity with SQL and experience querying large datasets
- Knowledge of model interpretability and bias mitigation techniques
- CHF [X–Y] gross annually based on experience; 13. Monatslohn (13th-month bonus) standard in Switzerland
- Flexible work location: [remote / [CITY], [CANTON]]
- [Add company-specific benefits: professional development budget, gym membership, team events, etc.]
Frequently asked questions
- Should we require a degree in computer science or statistics?
- Not necessarily. Strong practical skills, real projects, and demonstrable ML knowledge matter more than credentials. A bootcamp graduate with production experience may be a better fit than a PhD without deployment exposure. Screen on ability, not pedigree.
- What Pensum should we advertise?
- Most tech roles in Switzerland are 80–100%. Specify the exact percentage (e.g., 90%) to set expectations. If flexibility exists, state the range. Part-time ML roles are less common but possible for senior or specialized candidates.
- Where do we mention language requirements?
- Add language requirements in the must-haves section. In German-speaking cantons, German fluency is often implicit but worth stating. Specify 'Business-level German' or 'English sufficient' depending on team language and canton culture.
General information for Swiss employers, not legal advice. Have a lawyer confirm anything with legal consequences.