Job Description

We are seeking a skilled and experienced Machine Learning Engineer to join our engineering team. The ideal candidate will be responsible for designing, developing, and deploying machine learning models that solve complex real-world problems. You will work closely with our team to implement data-driven solutions and ensure these models are seamlessly integrated into production systems. This role requires a strong foundation in machine learning and software engineering to contribute throughout the project lifecycle - from idea to deployment and monitoring.

Responsibilities:

• Develop clean, efficient, and scalable code for training, evaluating, and deploying machine learning models

• Design and implement machine learning pipelines and production-ready solutions

• Test, monitor, and maintain ML systems in production environments

• Document model architecture, training procedures, and system design

• Provide technical support and insights on model performance and data integrity

• Collaborate with technical and non-technical colleagues to align solutions with business needs

• Participate in brainstorming sessions to identify the best technical approaches for real-world problems

• Continuously iterate on models and infrastructure to improve performance and reliability

Qualifications

Required skills:

• Solid experience with Python and machine learning frameworks such as PyTorch or TensorFlow

• Familiarity with data handling and preprocessing using libraries like Pandas, NumPy, or similar

• Understanding of machine learning concepts and algorithms (e.g., supervised/unsupervised learning, deep learning, model evaluation)

• Experience building and deploying models into production environments

• Proactive and responsible approach to work

• Strong problem-solving and analytical skills

• Knowledge of version control tools like Git

• Experience working in Linux environments

• Very good written and spoken English
  

Bonus points for:

• Experience with cloud platforms (e.g. AWS, GCP, Azure)

• Familiarity with Docker and containerized development

• Understanding of MLOps practices or DevOps in ML contexts

• Exposure to modern development workflows (e.g., CI/CD, testing pipelines)

• Experience with backend development frameworks (e.g., FastAPI, Flask)
  
  

What do we offer?

• An opportunity to work on technically challenging projects and products

• A diverse environment with agile and talented individuals across the career spectrum - to teach and be taught

• Experimenting with new technologies

• Flexible working hours (workday starts between 7 am and 10 am, as per your preferences)

• Unlimited coffee and drinks

• Parking space

• Friendly atmosphere and a pet-friendly office