Senior Machine Learning Engineer

What we offer:

• Flexible Working Hours: With a global team supporting mission-critical operations, we have to be at our best, so we’ve adopted flexible hours to allow for balance.
• Premium Healthcare and Health Insurance: Health and wellness are critical to living a happy and resilient life. We provide first-class medical, dental and vision coverage to you and your dependents.
• Competitive Salary & Equity: Our team is our biggest asset. We value the hard work that each person commits to us, so we provide competitive, transparent compensation packages.
• Generous relocation package to assure smooth relocation to Tricity area.
• High Energy Environment: We live by the mantra that now is better than never. You will find yourself surrounded by peers who constantly challenge the status quo.
• Flexible Time Off: We encourage you to take time off as you need it. While our team is hard-working, our success is measured by output—not time spent.
• Office, Equipment & Tools: We bring the best tools to the mission, from ergonomic desk setups to modern productivity software. We have a freshly prepared team breakfast and lunch every day.

How do we hire:

We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you.

What we value:

• Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain
• Strong understanding of data structures, algorithms, and software engineering best practices.
• Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts.
• Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools.
• Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines.
• A bias for simplicity and clarity in solving complex problems.
• Intellectual curiosity and willingness to collaborate.
• Clear communication and collaboration across cross-functional teams.