About the role
We’re hiring a Machine Learning Engineer to work on large-scale, real-world machine learning problems that directly impact how products and operations perform.
This is a hands-on engineering role, focused on building, deploying and improving ML models that run in production every day. You will work closely with software engineers, data engineers and product teams to turn complex problems into scalable ML solutions.
This is not a research-only position. Your models go live, are monitored, and are measured on impact.
What you’ll work on
You will work on applied ML use cases such as:
Forecasting & optimization problems (e.g. demand, capacity, planning)
Ranking, recommendation or decision systems that operate at scale
Translating data into features, models and production pipelines
Deploying ML models into production environments (batch and/or real-time)
Monitoring model performance, data quality and drift
Iterating on models based on real-world feedback and performance metrics
You’ll own the ML lifecycle end-to-end: problem framing → modeling → deployment → improvement.
Technical environment
You’ll work in a modern ML engineering setup, typically including:
Python as the main language
ML frameworks such as PyTorch, TensorFlow or scikit-learn
SQL and large-scale data processing
Docker, CI/CD and cloud infrastructure
Production environments where reliability, latency and scalability matter
Exact tooling may vary, but engineering quality and production-readiness are key.
Who you are
You are an engineer who enjoys working at the intersection of data, software and applied machine learning.
You bring:
Experience shipping machine learning models to production
Strong Python skills and solid engineering fundamentals
Comfort working with data at scale and reasoning about trade-offs
A pragmatic mindset: you care about impact, not just model accuracy
Clear communication and a collaborative approach
Why this role is interesting
Work on non-trivial ML problems with measurable real-world impact
Full ownership of models in production — not just experimentation
Collaboration with strong engineering and data teams
A culture that values quality, correctness and continuous improvement
Hybrid working setup with flexibility and autonomy
Interested?
Apply directly or reach out for an informal conversation.
We care about how you think and build, not just what’s on your CV.