Welcome to SMG Swiss Marketplace Group AG
We are a pioneering network of online marketplaces and a leading European digital company that simplifies people’s lives with forward-looking products.
A little bit about us to help ignite your imagination
SMG Swiss Marketplace Group AG came together following the merger of Scout24 Switzerland and TX Markets in 2021. A union which included some of Switzerland’s most well-known leading marketplaces.
Our exciting portfolio currently spans four business areas and includes Real Estate (immoscout24.ch, homegate.ch, ImmoStreet.ch, home.ch, Acheter-Louer.ch, Flatfox, Casasoft), Automotive (autoscout24.ch, motoscout24.ch), General Marketplaces (anibis.ch, tutti.ch, Ricardo.ch) and Finance and Insurance (financescout24.ch, Moneyland.ch).
At SMG Serbia, 130+ talents thrive in a fast-paced, collaborative environment, guided by our values: Better Together, Aim High, and Act Responsibly, shaping the future of digital marketplaces.
Job Description
As a Machine Learning Engineer at SMG’s Real Estate Business Unit, you will be instrumental in bridging the gap between data science research and reliable, scalable machine learning systems in production. This role is embedded within a cross-functional, agile data team that thrives on collaboration and innovation.
We are looking for a candidate with a background in data science/machine learning engineering who is passionate about engineering and automation, and who can bring analytical models from prototype stage to production at scale and with confidence. You will work on some of Switzerland’s most visited platforms like homegate.ch and immoscout24.ch, delivering business impact powered by machine learning.
About The Position
- Translate data science prototypes into production-ready machine learning systems, collaborating closely with data scientists.
- Build and maintain robust and scalable data pipelines for feature engineering, model training, inference, and monitoring.
- Own the full model lifecycle: CI/CD, deployment, model versioning, observability (including drift and SLA monitoring), and retraining.
- Optimize models for real-time applications, focusing on speed, reliability, and system integration (e.g., quantization, pruning).
- Partner with Data Engineers, Software & Platform Engineers to ensure the infrastructure can support modern ML workloads and latency-sensitive use cases.
- Contribute to the design of ML architecture and internal tools that empower the data science team to experiment and deploy models efficiently.
- Drive the adoption of engineering best practices within the data science team, including code reviews, testing frameworks, and reproducibility standards.
- Take part in stakeholder conversations to align on measurable success criteria, data availability, and production KPIs.
- Support and maintain production ML systems
Qualifications
- 3+ years of experience in machine learning engineering or MLOps roles with a prior foundation in applied data science.
- Solid experience building and deploying models in production environments with retraining workflows and observability in place.
- Strong proficiency in Python and SQL, with expertise in ML frameworks like Scikit-learn, LightFM, and orchestration tools like Airflow and MLflow.
- Experience working with cloud platforms and distributed training/inference tools (e.g., AWS SageMaker, ECS Fargate, Aurora Serverless).
- Familiarity with model performance optimization (e.g., latency reduction, throughput scaling) and real-time inference environments.
- Hands-on experience with feature store management, data versioning, and maintaining online/offline feature pipelines.
- Proficient with infrastructure-as-code and deployment tooling (e.g., Docker, CDK, CI/CD).
- Ability to work cross-functionally with Data Scientists, Engineers, and Product stakeholders to prioritize initiatives and solve real business problems.
- Excellent problem-solving, communication, and teamwork skills in English.
- Curious mindset and proactive learner who stays up-to-date with the latest advances in ML systems, tooling, and infrastructure.
Additional Information
Recruitment Process
We keep hiring simple and human:
- Initial Video-Call with our Recruiter (30min)
- Meet the Hiring Manager (60min)
- Case Study (with home assignment & Technical Interview (60min))
- Team Fit Conversation (60min)
At SMG, we value unique perspectives and believe in the potential of every individual. Together, we learn from one another, embrace differences, and drive real impact.
Benefits you'll love:
- Be a part of well established Swiss-based subsidiary in Belgrade
- We value work-life balance and employee happiness. That’s why we grant you hybrid work environment, and 25 vacation days per year, with the option to buy another 10 days in an unbureaucratic process. You get 5 fully paid sick days per year, and another 5 days for special occasions
- Lots of team activities and perks
- Great equipment, competitive salary and a variety of fringe benefits like a gym membership and discounts at local stores
- International team and working environment of talented and fun people
- Stimulating work experience that will allow you to grow both professionally and personally
- Yearly budget for your personal development
Disclaimer
SMG Swiss Marketplace Group AG primarily engages directly with candidates. We do not accept unsolicited resumes or candidate profiles from recruitment agencies unless we have specifically requested their assistance for particular roles. Any submissions made without a prior request from our Talent Acquisition team will not be acknowledged or considered. We appreciate the understanding and cooperation of recruitment agencies in respecting this policy.
